{"as_of":"2026-08-06T18:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6f568a0dcb7c1a8b226d851ed0ba9fdeff91a2a17fe7bd20d5f9905c2d08215d","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-15T09:26:52.783241Z","state":"measured"},{"denominator":101,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":101,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":61,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":61,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:42:28.596981Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":622,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2303.17564","last_updated":"2023-12-21T06:21:11Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-03-30T17:30:36Z","title":"BloombergGPT: A Large Language Model for Finance","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-13T23:19:46.231145Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2303.17564"},"observation_digest":"sha256:2a3258385eb7be644e2a484b97649fb2ed2c29a0a624e185de72e1242394b4a2","observation_id":"586bfbf2-90ef-4158-97ed-d0b4b7e74f83","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2306.00890","last_updated":"2023-06-01T16:50:07Z","snapshot_observed_at":"2026-07-29T15:30:39.490502Z","submitted_at":"2023-06-01T16:50:07Z","title":"LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-24T08:33:16.436778Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2306.00890"},"observation_digest":"sha256:efcfa7b0a439918f6d4434bb0894eba20910d30821557950359bf77348d7287a","observation_id":"e2e18880-bc2b-40b1-a290-5ba0e227068b","resolution":{"observed_at":"2026-05-24T08:34:11.698181Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2501.02407","last_updated":"2026-05-20T14:04:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-05T00:03:18Z","title":"Towards the Anonymization of the Language Modeling","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-23T06:28:16.975305Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2501.02407"},"observation_digest":"sha256:fdc1eff94e87a588024247261e01dad307ca73c646c2786b94f01c3722d8807f","observation_id":"aaefe1a8-c2f1-43a9-b4e0-34d2c7d75c02","resolution":{"observed_at":"2026-05-23T06:32:39.492294Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-06T16:42:28.596981Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.12774","last_updated":"2025-07-17T04:31:55Z","snapshot_observed_at":"2026-08-06T16:36:30.473430Z","submitted_at":"2025-07-17T04:31:55Z","title":"A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models","version":1},"reference_index":107,"source":"pdf_text","source_observed_at":"2026-08-06T16:42:28.596981Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2507.12774"},"observation_digest":"sha256:5e41328e304ddd376879689b7d11a53375cbe0fadf6b7d72c849a390391459eb","observation_id":"f257985c-96cd-406b-864a-7b6f95edf05b","resolution":{"observed_at":"2026-08-06T16:42:28.596981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-06T16:13:18.348427Z","title":"Clinicalbert: Modeling clinical notes and predicting hospital readmission,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T15:59:24.584987Z","submitted_at":"2025-07-18T17:00:27Z","title":"DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:18.348427Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:e0d40b3dcfe57ac9de7dafee7fe0dca131d6d25eb365c18069bd5d3e149d01e7","observation_id":"d9a203b6-d7fd-4434-9ab5-7140e263b176","resolution":{"observed_at":"2026-08-06T16:13:18.348427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-06T14:25:56.530918Z","title":"Clinicalbert: Modeling clinical notes and predicting hospital readmission","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2507.19407","last_updated":"2025-08-06T10:08:06Z","snapshot_observed_at":"2026-08-06T14:25:55.297707Z","submitted_at":"2025-07-25T16:15:00Z","title":"Towards Domain Specification of Embedding Models in Medicine","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T14:25:56.530918Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2507.19407"},"observation_digest":"sha256:85bcea3a076b0a587737c93e87946528fede61bccff408c54d3f76eeee35a05c","observation_id":"eaa76c6d-078d-48b8-9a11-cdec9904b5fa","resolution":{"observed_at":"2026-08-06T14:25:56.530918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-06T10:55:14.723015Z","title":"Clinicalbert: Modeling clinical notes and predicting hospital readmission","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2507.23276","last_updated":"2025-08-01T12:49:36Z","snapshot_observed_at":"2026-08-06T10:54:57.937285Z","submitted_at":"2025-07-31T06:32:06Z","title":"How Far Are AI Scientists from Changing the World?","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-06T10:55:14.723015Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2507.23276"},"observation_digest":"sha256:56228ec37f4c201aeecf8dbf6474a9b489d32c68ad569eecd4d5f4da72bb24b0","observation_id":"ca5c3230-1202-4cb7-90a1-72c6331872c7","resolution":{"observed_at":"2026-08-06T10:55:14.723015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-06T10:08:11.410638Z","title":null,"venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2508.00581","last_updated":"2025-08-01T12:24:49Z","snapshot_observed_at":"2026-08-06T10:08:09.832725Z","submitted_at":"2025-08-01T12:24:49Z","title":"From EMR Data to Clinical Insight: An LLM-Driven Framework for Automated Pre-Consultation Questionnaire Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T10:08:11.410638Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2508.00581"},"observation_digest":"sha256:2d80abc9084a83c4038cefe01a02edb3ed8e9b046864d07861748af65d47d3a0","observation_id":"cfad807e-5b1f-44fc-9e59-a36bf98856a6","resolution":{"observed_at":"2026-08-06T10:08:11.410638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-06T05:37:35.157345Z","title":"Clini- calbert: Modeling clinical notes and predicting hospital read- mission.arXiv:1904.05342, 2019","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2508.01453","last_updated":"2025-08-02T18:02:44Z","snapshot_observed_at":"2026-08-06T05:37:34.467512Z","submitted_at":"2025-08-02T18:02:44Z","title":"Kernel-Based Sparse Additive Nonlinear Model Structure Detection through a Linearization Approach","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T05:37:35.157345Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2508.01453"},"observation_digest":"sha256:2064039b1fd13aceb3a54dabd1af469eb6873ddfb6d4dfea60575c0f6d5bd5cd","observation_id":"76dccddd-2317-4a99-8d46-8b6e4c9eaf3b","resolution":{"observed_at":"2026-08-06T05:37:35.157345Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-06T13:37:59.915837Z","title":"arXiv e-prints , page arXiv:1904.05342","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2508.03718","last_updated":"2025-07-28T00:22:03Z","snapshot_observed_at":"2026-08-06T13:37:55.710776Z","submitted_at":"2025-07-28T00:22:03Z","title":"Health Insurance Coverage Rule Interpretation Corpus: Law, Policy, and Medical Guidance for Health Insurance Coverage Understanding","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T13:37:59.915837Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2508.03718"},"observation_digest":"sha256:c4f02abf66b3a6495dc2cb7a8f340ee9eabf38bff5474651d3d0704aefa6191e","observation_id":"e7b67e51-8866-40db-8cf4-904aca62fff4","resolution":{"observed_at":"2026-08-06T13:37:59.915837Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T23:40:43.825872Z","title":"Clinicalbert: Modeling clinical notes and predicting hospital readmission","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2508.05019","last_updated":"2025-08-07T04:12:43Z","snapshot_observed_at":"2026-08-06T05:45:28.428779Z","submitted_at":"2025-08-07T04:12:43Z","title":"Skin-SOAP: A Weakly Supervised Framework for Generating Structured SOAP Notes","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-05T23:40:43.825872Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2508.05019"},"observation_digest":"sha256:1d7e247ddf08189035849de2bd0289431b5bc3d754a7785b6049893a7deba40d","observation_id":"255cd817-a2ff-4c39-92a0-1738d5c5043b","resolution":{"observed_at":"2026-08-05T23:40:43.825872Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T20:06:13.383624Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.11285","last_updated":"2025-08-15T07:47:10Z","snapshot_observed_at":"2026-08-05T20:06:10.279471Z","submitted_at":"2025-08-15T07:47:10Z","title":"AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queries","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T20:06:13.383624Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2508.11285"},"observation_digest":"sha256:04f35b9c8edb2263ffb945b8dfa76ec674c0a88796fe3780c0c7dd7e42ce918b","observation_id":"a949f4e8-6ce3-4742-ae3c-2e977029d5ab","resolution":{"observed_at":"2026-08-05T20:06:13.383624Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T18:40:18.750669Z","title":", Altosaar , J","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.14357","last_updated":"2025-08-20T01:58:45Z","snapshot_observed_at":"2026-08-05T18:40:16.611262Z","submitted_at":"2025-08-20T01:58:45Z","title":"Organ-Agents: Virtual Human Physiology Simulator via LLMs","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-05T18:40:18.750669Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2508.14357"},"observation_digest":"sha256:672790f982639433acbf5212240cb7adba649da423439aee966aee393a98a0be","observation_id":"98ef1993-1955-42ee-9719-1ae1ab8f79d3","resolution":{"observed_at":"2026-08-05T18:40:18.750669Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T12:08:08.330308Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.01899","last_updated":"2025-09-02T02:39:42Z","snapshot_observed_at":"2026-08-05T12:08:06.210707Z","submitted_at":"2025-09-02T02:39:42Z","title":"Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-05T12:08:08.330308Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2509.01899"},"observation_digest":"sha256:7ea4002277eea2577a19c6f646592e6da463b73a31f3623013a6907dc73d2555","observation_id":"3fd88e18-fc2e-4e0f-9c79-6722e8a4476b","resolution":{"observed_at":"2026-08-05T12:08:08.330308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T11:12:21.845184Z","title":"Clinicalbert: Modeling clinical notes and predict- ing hospital readmission,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2509.04519","last_updated":"2025-09-03T10:12:18Z","snapshot_observed_at":"2026-08-05T11:12:21.355405Z","submitted_at":"2025-09-03T10:12:18Z","title":"Hierarchical Section Matching Prediction (HSMP) BERT for Fine-Grained Extraction of Structured Data from Hebrew Free-Text Radiology Reports in Crohn's Disease","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:21.845184Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2509.04519"},"observation_digest":"sha256:b8352e0b4457eab43ff24987291371ed52a304f56983837bf9eaf7abc391c169","observation_id":"5c03c321-94da-41bc-87cd-e7433f8ba2c9","resolution":{"observed_at":"2026-08-05T11:12:21.845184Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-04T23:23:40.126983Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.06650","last_updated":"2025-09-08T13:04:07Z","snapshot_observed_at":"2026-08-06T17:44:40.601257Z","submitted_at":"2025-09-08T13:04:07Z","title":"Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:40.126983Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2509.06650"},"observation_digest":"sha256:69f452f1e7bfd34e86edb77e351ac88a0ea8498b8c0476b1cadd61d3a34e597c","observation_id":"b4bebb46-5f29-4a1f-8b0e-bb91749ecb03","resolution":{"observed_at":"2026-08-04T23:23:40.126983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-04T16:45:44.489694Z","title":"Clinicalbert: Modeling clinical notes and predicting hospital readmission,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2509.11924","last_updated":"2025-09-16T02:04:27Z","snapshot_observed_at":"2026-08-06T18:35:20.038424Z","submitted_at":"2025-09-15T13:38:35Z","title":"Enriched text-guided variational multimodal knowledge distillation network (VMD) for automated diagnosis of plaque vulnerability in 3D carotid artery MRI","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T16:45:44.489694Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2509.11924"},"observation_digest":"sha256:398b47742e2fba9934aeeaacb5f8f1b083d126715a395dc29f073e58b7a09d82","observation_id":"1ac0c000-8749-4f4f-8d53-60311a962877","resolution":{"observed_at":"2026-08-04T16:45:44.489694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-03T22:24:06.785052Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.10887","last_updated":"2026-06-19T10:45:44Z","snapshot_observed_at":"2026-08-03T22:23:55.088851Z","submitted_at":"2025-11-14T01:49:24Z","title":"MedPath: Multi-Domain Cross-Vocabulary Hierarchical Paths for Biomedical Entity Linking","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-03T22:24:06.785052Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2511.10887"},"observation_digest":"sha256:e43795bb76d17a078b3a388b7e8ed757b9c8337f871f169c404b54d5299a5db9","observation_id":"d5936e4e-1de8-411b-8fff-df160a7ff09c","resolution":{"observed_at":"2026-08-03T22:24:06.785052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2601.09722","last_updated":"2026-05-18T07:15:17Z","snapshot_observed_at":"2026-08-04T01:21:01.250320Z","submitted_at":"2025-12-27T10:00:52Z","title":"ADMEDTAGGER: an annotation framework for distillation of expert knowledge for the Polish medical language","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-21T15:53:11.348103Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2601.09722"},"observation_digest":"sha256:f2b482c052c5c7aa631d5dc77c171e4660bbaf38098fa2174a6d755a5e39d2fc","observation_id":"07a54525-1e35-43bd-90ef-22be30088a5a","resolution":{"observed_at":"2026-05-21T15:54:14.527383Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2604.03259","last_updated":"2026-03-12T04:03:27Z","snapshot_observed_at":"2026-07-06T22:52:29.705312Z","submitted_at":"2026-03-12T04:03:27Z","title":"From Pre-trained Models to Large Language Models: A Comprehensive Survey of AI-Driven Psychological Computing","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-15T12:36:05.547383Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2604.03259"},"observation_digest":"sha256:bb3f143d7392459654ba0105a74cfb88de942f68f7b19a49a08812de39fb6211","observation_id":"50a6f403-7652-4fb7-a4da-f02f906ae57f","resolution":{"observed_at":"2026-05-15T12:40:00.385911Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2604.07274","last_updated":"2026-04-08T16:37:22Z","snapshot_observed_at":"2026-08-02T20:03:31.551355Z","submitted_at":"2026-04-08T16:37:22Z","title":"A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T17:51:41.539951Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2604.07274"},"observation_digest":"sha256:ca9a8fac813838f42f5a31b98e7ef05c77eb35ad330335b9709de4ccfb3d4cc2","observation_id":"91ef4cd9-fdfe-4ef2-a58e-c843435bb0d8","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2604.09975","last_updated":"2026-04-11T01:15:07Z","snapshot_observed_at":"2026-07-06T22:58:43.155246Z","submitted_at":"2026-04-11T01:15:07Z","title":"EncFormer: Secure and Efficient Transformer Inference over Encrypted Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T16:56:54.499046Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2604.09975"},"observation_digest":"sha256:45b7093a31fae31d8fb8fdb8617d1f645693473aff9c1ff9155a496c3b80a05d","observation_id":"303c3e06-205b-4cad-80ea-80d98236bc01","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2604.10783","last_updated":"2026-05-25T13:18:36Z","snapshot_observed_at":"2026-07-12T22:21:23.393919Z","submitted_at":"2026-04-12T19:18:02Z","title":"Learning Preference-Based Objectives from Clinical Narratives for Dynamic Sepsis Treatment","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T15:38:47.477293Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2604.10783"},"observation_digest":"sha256:0d4179822ce3f629f085dc1e191021d8f1c4c6bcca49611d02598f9f2ac9cc04","observation_id":"5afa7749-afb7-403d-b93b-bcee7172dfe5","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2604.11137","last_updated":"2026-04-19T09:00:50Z","snapshot_observed_at":"2026-07-06T22:59:36.571641Z","submitted_at":"2026-04-13T07:49:39Z","title":"From Answers to Arguments: Toward Trustworthy Clinical Diagnostic Reasoning with Toulmin-Guided Curriculum Goal-Conditioned Learning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T16:30:34.434984Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2604.11137"},"observation_digest":"sha256:be7a5f18ffd1900a570af1662e965768869947a19464562ae7886517ed75ec5e","observation_id":"e68e8f7d-f6f5-4e48-951d-14dfe34f13bf","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2604.14651","last_updated":"2026-04-23T19:52:27Z","snapshot_observed_at":"2026-07-06T23:02:22.790426Z","submitted_at":"2026-04-16T05:58:37Z","title":"CURA: Clinical Uncertainty Risk Alignment for Language Model-Based Risk Prediction","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T11:58:02.994749Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2604.14651"},"observation_digest":"sha256:6c10123152fe07ce20ca88703d0eff58aaf481d70dbcfeb3c9a49499595f5fee","observation_id":"9f2cac3f-ffe9-4681-a547-ee0a4c39788c","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2604.16364","last_updated":"2026-03-21T20:56:25Z","snapshot_observed_at":"2026-08-02T23:48:00.268626Z","submitted_at":"2026-03-21T20:56:25Z","title":"Clinical Note Bloat Reduction for Efficient LLM Use","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-15T06:40:23.199798Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2604.16364"},"observation_digest":"sha256:80c6d77158505485273dc81f5710d4adb8cedb7fb55a8c8694aa943a7a111a95","observation_id":"553e3475-6113-408d-ae0f-40dc457c7eb7","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2604.16378","last_updated":"2026-06-20T20:02:32Z","snapshot_observed_at":"2026-08-06T12:56:36.793440Z","submitted_at":"2026-03-24T20:21:31Z","title":"Reciprocal Co-Training (RCT): Coupling Gradient-Based and Non-Differentiable Models via Reinforcement Learning","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-15T00:13:01.186687Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2604.16378"},"observation_digest":"sha256:ce74fad3df7c01b4092af120cad1a6e493924fe39a02cd598ed34f8b821b6fed","observation_id":"960596ac-8c5b-47b8-ac32-6459143f1259","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2604.18752","last_updated":"2026-07-01T01:21:51Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-20T18:55:15Z","title":"A Scientific Human-Agent Reproduction Pipeline","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-05T10:35:33.834110Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2604.18752"},"observation_digest":"sha256:ee1773ca51c92f0a8420c2fe2efa7c1928370a7a85b33c1017605b12f10ee9ab","observation_id":"7c1f7884-655d-46c8-a82a-7de69d7e2247","resolution":{"observed_at":"2026-07-05T10:40:55.185011Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2604.18753","last_updated":"2026-05-06T22:51:05Z","snapshot_observed_at":"2026-08-03T19:16:52.460241Z","submitted_at":"2026-04-20T18:55:56Z","title":"Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-10T05:49:08.511479Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2604.18753"},"observation_digest":"sha256:c48cebba1c7c30daaeaba6cd9f0adf1b08b71449579cdfcc4eb3cc071c859c55","observation_id":"ed859cc1-2f18-4236-ad34-fa80aeaee32a","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2604.27319","last_updated":"2026-04-30T02:03:37Z","snapshot_observed_at":"2026-07-06T23:12:47.745700Z","submitted_at":"2026-04-30T02:03:37Z","title":"REBENCH: A Procedural, Fair-by-Construction Benchmark for LLMs on Stripped-Binary Types and Names (Extended Version)","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-07T09:32:02.956530Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2604.27319"},"observation_digest":"sha256:4f52d32f4dc483358e3a8000b0e8a8beb94e55b564ff7c78bb3b83308b894e44","observation_id":"8cb021e6-7995-4f4c-8f0c-2babe49a2f18","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2605.00708","last_updated":"2026-05-01T14:51:55Z","snapshot_observed_at":"2026-07-06T23:14:06.327027Z","submitted_at":"2026-05-01T14:51:55Z","title":"Deep Kernel Learning for Stratifying Glaucoma Trajectories","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-09T19:32:15.656522Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2605.00708"},"observation_digest":"sha256:c22002864859416028cacaf309860d44f7418908162d3908db196880fbaa2817","observation_id":"a0a4bec8-8815-4a42-a789-83c809ec363d","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2605.01664","last_updated":"2026-05-03T01:33:31Z","snapshot_observed_at":"2026-08-02T09:22:54.253866Z","submitted_at":"2026-05-03T01:33:31Z","title":"A Hybrid Retrieval and Reranking Framework for Evidence-Grounded Retrieval-Augmented Generation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-09T17:16:00.567990Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2605.01664"},"observation_digest":"sha256:916d2ff4e99406b2739aad6e2b1041292b36285f687e07ab254a7202879a4aaf","observation_id":"82e276e2-4790-41e9-83a4-4f5849f634e5","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2605.05493","last_updated":"2026-05-06T22:27:50Z","snapshot_observed_at":"2026-08-03T00:00:48.387252Z","submitted_at":"2026-05-06T22:27:50Z","title":"A renormalization-group inspired lattice-based framework for piecewise generalized linear models","version":1},"reference_index":284,"source":"arxiv_source","source_observed_at":"2026-05-08T15:49:33.695290Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2605.05493"},"observation_digest":"sha256:ac7b762969442ff316e0a3c50b368f05d44b873a515849e622c22729fdbdf6b2","observation_id":"66a3e8c9-f57a-41f8-be06-0053af97c74c","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2605.06173","last_updated":"2026-05-08T14:55:12Z","snapshot_observed_at":"2026-08-04T01:45:39.535537Z","submitted_at":"2026-05-07T12:54:53Z","title":"Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-08T13:48:17.815440Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2605.06173"},"observation_digest":"sha256:18f8f9c313b2c9ab4d17e46bc4e0be43acfdee7b115deb423aed4280fecd6e6f","observation_id":"18368ed1-ac88-44ad-b3cf-cf3f25760f6a","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2605.06173","last_updated":"2026-05-08T14:55:12Z","snapshot_observed_at":"2026-08-04T01:45:39.535537Z","submitted_at":"2026-05-07T12:54:53Z","title":"Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-11T02:03:15.389641Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2605.06173"},"observation_digest":"sha256:73c7052bd7b0243fa8ab77a78a14ebfa81293b2fe582d273aababce016d31fbb","observation_id":"632e6225-43c2-4e21-ab7c-4522a7c98f36","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2605.06191","last_updated":"2026-05-07T13:05:07Z","snapshot_observed_at":"2026-07-06T23:18:41.400741Z","submitted_at":"2026-05-07T13:05:07Z","title":"Systematic Evaluation of Large Language Models for Post-Discharge Clinical Action Extraction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-08T10:15:03.503011Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2605.06191"},"observation_digest":"sha256:f22e94d6e7a05e32c960821604dd7a7015080182fbae81245c94d50e4d9a5c82","observation_id":"82571079-586e-49b6-aac7-9e5848907352","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2605.08045","last_updated":"2026-05-08T17:35:41Z","snapshot_observed_at":"2026-07-06T23:20:19.821342Z","submitted_at":"2026-05-08T17:35:41Z","title":"Uncertainty-Aware Structured Data Extraction from Full CMR Reports via Distilled LLMs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-11T02:16:55.470827Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2605.08045"},"observation_digest":"sha256:b83a5bd244af9aa3a7f2c7e5727cf73ed51f1638639f8694ae041920119a2c9e","observation_id":"c817363b-de41-4c97-8e21-dd8ad4ae6897","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2605.10286","last_updated":"2026-05-11T09:46:41Z","snapshot_observed_at":"2026-07-06T23:22:18.704329Z","submitted_at":"2026-05-11T09:46:41Z","title":"AgentRx: A Benchmark Study of LLM Agents for Multimodal Clinical Prediction Tasks","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-05-12T05:10:02.941396Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2605.10286"},"observation_digest":"sha256:59e80a0e3156bb0f326f34dd4d3a102388bfdfc1f3056a0374eea3db5c444de1","observation_id":"7f28da05-2d7c-4cd3-80df-b001161a214a","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2605.10813","last_updated":"2026-05-11T16:33:47Z","snapshot_observed_at":"2026-07-30T22:58:47.084744Z","submitted_at":"2026-05-11T16:33:47Z","title":"NanoResearch: Co-Evolving Skills, Memory, and Policy for Personalized Research Automation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-12T04:12:49.742272Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2605.10813"},"observation_digest":"sha256:6b35626a7552451349cb9c06fbe107051978764e5947a2a894553436b8a1d493","observation_id":"978bf3b7-d4a5-4838-8cb8-714cd2ea0592","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2605.12817","last_updated":"2026-05-12T23:18:14Z","snapshot_observed_at":"2026-07-06T23:24:27.821980Z","submitted_at":"2026-05-12T23:18:14Z","title":"Training Large Language Models to Predict Clinical Events","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-14T20:01:35.179487Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2605.12817"},"observation_digest":"sha256:9ebae4014a4b9988b5a40465b0d9e5a78aeb229256906e760296185905bea8f4","observation_id":"fed6b870-8996-42ef-b2a4-fe736225b0e2","resolution":{"observed_at":"2026-05-15T09:26:53.041773Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2605.17775","last_updated":"2026-05-18T02:49:04Z","snapshot_observed_at":"2026-07-06T23:28:45.646975Z","submitted_at":"2026-05-18T02:49:04Z","title":"Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-20T11:44:58.613981Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2605.17775"},"observation_digest":"sha256:2e3c4e046e05b966e6f791fc42ecd8157963e57bac8ddb090cf5878d43d2246e","observation_id":"cac0c62b-0549-438a-8d50-865ebb0cfa19","resolution":{"observed_at":"2026-05-20T11:48:15.125040Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2605.30337","last_updated":"2026-05-28T17:59:01Z","snapshot_observed_at":"2026-08-06T03:38:35.973424Z","submitted_at":"2026-05-28T17:59:01Z","title":"Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-29T08:58:52.511363Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2605.30337"},"observation_digest":"sha256:4417e6f5f50a7920d8f25c70d26b099946ec4efeaaf076926f35dbf58ea8c6f7","observation_id":"9f23342b-7d52-42fc-a394-fb19b0b0552c","resolution":{"observed_at":"2026-06-29T09:03:15.958188Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2605.30962","last_updated":"2026-05-29T07:57:11Z","snapshot_observed_at":"2026-08-02T21:54:04.082762Z","submitted_at":"2026-05-29T07:57:11Z","title":"Sequence models reveal diagnosis accumulation pathways beyond comorbidity burden in population-scale hospital data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T20:15:13.494324Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2605.30962"},"observation_digest":"sha256:5d097b7851b86e8543da155ab6e033ef0460a061ec6d323d03161ef95ebeeba1","observation_id":"71a73f4c-98a3-47cf-af04-fd2fa8ffa45e","resolution":{"observed_at":"2026-06-28T20:22:37.241891Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2606.01904","last_updated":"2026-06-02T06:52:52Z","snapshot_observed_at":"2026-08-06T17:55:13.497262Z","submitted_at":"2026-06-01T08:42:55Z","title":"KliniskVestBERT: BERT Model Specialised to Norwegian Clinical Texts","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T14:32:23.329860Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2606.01904"},"observation_digest":"sha256:50dc17118eb8f04aaeafac3767e87703e4a7053e4b34ab0fcd2c4fe3c8a49032","observation_id":"cb9a7cc9-97e0-40fa-9ab0-867c293cdf54","resolution":{"observed_at":"2026-07-01T23:16:23.979947Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-07-12T19:39:46.922877Z","title":"& Ranganath, R","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2606.05174","last_updated":"2026-04-17T03:08:55Z","snapshot_observed_at":"2026-08-04T02:46:52.030357Z","submitted_at":"2026-04-17T03:08:55Z","title":"Improving Heart-Focused Medical Question Answering in LLMs via Variance-Aware Rubric Rewards with GRPO","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-12T19:39:46.922877Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2606.05174"},"observation_digest":"sha256:82df4623aace5a3b583494e72080e9d4cb4c3401f2d3994a69ef0cdab2865092","observation_id":"bb23936f-9cce-4330-b999-895a4705171c","resolution":{"observed_at":"2026-07-12T19:39:46.922877Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2606.05436","last_updated":"2026-06-03T20:58:43Z","snapshot_observed_at":"2026-07-06T23:45:28.379468Z","submitted_at":"2026-06-03T20:58:43Z","title":"Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-06-28T06:03:59.798126Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2606.05436"},"observation_digest":"sha256:a2bc6afcb5d72dfedc99cb77b3cfcfd443d1b40be3fc73f93c854f17c6e1f329","observation_id":"b09f29ac-4dbe-4405-8f34-f854e51f99f1","resolution":{"observed_at":"2026-07-02T08:26:48.120789Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2606.10796","last_updated":"2026-06-09T12:44:12Z","snapshot_observed_at":"2026-08-02T13:50:09.400298Z","submitted_at":"2026-06-09T12:44:12Z","title":"Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-06-27T13:35:20.280375Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2606.10796"},"observation_digest":"sha256:3198d84f14ee06a23563fa0abc07fb8fc01351b6a87b7c07f0e696c6d3f4f610","observation_id":"41b29d2d-cc00-463d-914d-57492bf3d84c","resolution":{"observed_at":"2026-07-03T04:47:38.689822Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2606.20553","last_updated":"2026-06-18T17:58:25Z","snapshot_observed_at":"2026-08-06T06:29:53.553907Z","submitted_at":"2026-06-18T17:58:25Z","title":"From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-26T16:51:07.028013Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2606.20553"},"observation_digest":"sha256:1420bd28ae8bb91ed7b167f443910b160fe4a7bd7f49c00a062d27fdcd507bca","observation_id":"e3d488d9-e430-48d9-859e-c13c31938b7d","resolution":{"observed_at":"2026-07-04T04:39:35.034768Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2606.27174","last_updated":"2026-06-25T15:41:45Z","snapshot_observed_at":"2026-08-06T10:14:32.411723Z","submitted_at":"2026-06-25T15:41:45Z","title":"RecallRisk-BERT: A Multi-Task Framework for Post-Report Medical Device Recall Triage","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-06-26T05:03:30.360702Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2606.27174"},"observation_digest":"sha256:db9cd9176f4350896cba70ad3b4bce18516763c53220b9de211ef6103e0d372f","observation_id":"78e9fa21-402a-4fd9-a2c2-53a91eae81e6","resolution":{"observed_at":"2026-07-04T13:39:50.620570Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2606.28329","last_updated":"2026-05-19T15:18:23Z","snapshot_observed_at":"2026-07-07T00:02:29.000757Z","submitted_at":"2026-05-19T15:18:23Z","title":"$M^3 QuestionIng$: Multi-modal Multi-span Medical Question Answering","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-30T18:09:46.506953Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2606.28329"},"observation_digest":"sha256:1d883e168e8901dad659ccfc5eb8d73c9c26f2d984a6fa1082b72125ea2fe8d2","observation_id":"74afdc7c-4863-4768-b2e2-d7f052bdc77d","resolution":{"observed_at":"2026-06-30T18:15:00.019832Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2606.31245","last_updated":"2026-06-30T07:21:06Z","snapshot_observed_at":"2026-08-05T09:21:09.422349Z","submitted_at":"2026-06-30T07:21:06Z","title":"HyperVLP: Enhancing Hierarchical Surgical Video-Language Pre-training in Hyperbolic Space","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-01T05:58:11.210464Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2606.31245"},"observation_digest":"sha256:718a78be3a0cb2e7cc155993d3b53bebf4dccddbec8bd44c068f87d4aa93feac","observation_id":"70a3ab95-8383-4b3e-86ff-0ebf7e75a353","resolution":{"observed_at":"2026-07-01T09:55:41.610544Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2607.06127","last_updated":"2026-07-09T11:02:03Z","snapshot_observed_at":"2026-08-02T10:22:41.693734Z","submitted_at":"2026-07-07T10:37:04Z","title":"Measuring the practice of shared-decision making (OPTION12): An Investigation into Open-sourced Smaller LLMs (OS-sLLMs) for Better Privacy and Sustainability","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-07-08T16:03:00.061804Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2607.06127"},"observation_digest":"sha256:4d429647e061213c832e44e99e5514c404e7a3a8c10958c016877d8c05f301dd","observation_id":"95ad9b7d-786d-4070-b1d4-7e89e4160e61","resolution":{"observed_at":"2026-07-08T16:05:06.164808Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2607.06127","last_updated":"2026-07-09T11:02:03Z","snapshot_observed_at":"2026-08-02T10:22:41.693734Z","submitted_at":"2026-07-07T10:37:04Z","title":"Measuring the practice of shared-decision making (OPTION12): An Investigation into Open-sourced Smaller LLMs (OS-sLLMs) for Better Privacy and Sustainability","version":2},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-07-11T01:18:34.785275Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2607.06127"},"observation_digest":"sha256:bd24fd9b50fdd867b6abaf38f28d18f6ac31a4b75f98339da461261df87000d5","observation_id":"084358da-7a56-4711-a031-f857ec1b8ab0","resolution":{"observed_at":"2026-07-11T01:27:46.329898Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2607.06531","last_updated":"2026-07-07T17:38:47Z","snapshot_observed_at":"2026-07-10T23:18:28.190102Z","submitted_at":"2026-07-07T17:38:47Z","title":"The Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-07-08T02:59:58.330667Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2607.06531"},"observation_digest":"sha256:6f94e140e9eba946c41bbf8f69907ea82345c1d6556911785ee327c496587d0d","observation_id":"a25716a5-e84b-4382-9e1c-4ffb8394c9a0","resolution":{"observed_at":"2026-07-08T03:04:28.414969Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":"1904.05342","doi":"10.48550/arxiv.1904.05342","metadata_source":"pith","pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","venue":"cs.CL","work_id":"997be8af-a070-4f1c-8458-8e75c904f12c","year":2019},"citing_paper":{"arxiv_id":"2607.07761","last_updated":"2026-07-08T15:19:37Z","snapshot_observed_at":"2026-08-01T15:00:20.669918Z","submitted_at":"2026-07-08T15:19:37Z","title":"Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-10T18:50:22.827472Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2607.07761"},"observation_digest":"sha256:b479a72980aa739ee7f1d57408db313ea676f5df2f52ac2756d86e905301e6b5","observation_id":"091fb081-d407-4ba2-9eab-7effe5307aed","resolution":{"observed_at":"2026-07-10T18:57:31.628748Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-02T12:34:40.764157Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18270","last_updated":"2026-06-02T04:32:32Z","snapshot_observed_at":"2026-08-05T10:29:44.321626Z","submitted_at":"2026-06-02T04:32:32Z","title":"Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T12:34:40.764157Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2607.18270"},"observation_digest":"sha256:ba7f07a848bfe9112fa6ab35e22dec09aa26691652af107280068ad7a426b959","observation_id":"9814c1f2-cb06-4a2e-8451-ef607790243a","resolution":{"observed_at":"2026-08-02T12:34:40.764157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-07-31T16:40:24.220008Z","title":"ClinicalBERT: Modeling clinical notes and predicting hospital readmission,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2607.24371","last_updated":"2026-07-27T12:45:27Z","snapshot_observed_at":"2026-08-05T11:12:46.363484Z","submitted_at":"2026-07-27T12:45:27Z","title":"Closed-Loop Validation-Repair for Healthcare Interoperability: A Multi-Model Study of Schema Compliance in Clinical LLMs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-31T16:40:24.220008Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2607.24371"},"observation_digest":"sha256:300a33ba9ddd9b3ba834bfa5f4bbf0f8a34ee468c3b04f045c3ada38723f54a2","observation_id":"e3fe31af-c5d8-4e56-8c59-8cff2985d035","resolution":{"observed_at":"2026-07-31T16:40:24.220008Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-02T06:22:29.513987Z","title":"ClinicalBERT: Modeling clinical notes and predicting hospital readmission,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2607.24814","last_updated":"2026-07-14T13:44:20Z","snapshot_observed_at":"2026-08-02T06:22:28.362514Z","submitted_at":"2026-07-14T13:44:20Z","title":"Aletheia: An Offline-First Clinical Decision Support System for Differential Diagnosis in Low-Resource Healthcare Settings","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T06:22:29.513987Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2607.24814"},"observation_digest":"sha256:2cb31f8b63e75e51c2b3995377e71ef63e2c2e625ff81b854e1864de4f87306a","observation_id":"44fd1864-96eb-4a29-a92c-9b926ece6c27","resolution":{"observed_at":"2026-08-02T06:22:29.513987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-01T03:19:39.246043Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.25164","last_updated":"2026-07-28T00:25:05Z","snapshot_observed_at":"2026-08-03T19:35:07.614470Z","submitted_at":"2026-07-28T00:25:05Z","title":"OrganLens: Organ-Specific Representation Learning for CT Foundation Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T03:19:39.246043Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2607.25164"},"observation_digest":"sha256:b08c78402b5d102591e51c3b56054f11d50ac0840b6a81035f5f693595373687","observation_id":"75076bbe-25c1-4374-aecd-1a3107e31e33","resolution":{"observed_at":"2026-08-01T03:19:39.246043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-01T03:02:43.850182Z","title":"& Ranganath, R","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2607.25242","last_updated":"2026-08-03T23:54:14Z","snapshot_observed_at":"2026-08-06T17:45:51.762752Z","submitted_at":"2026-07-28T03:35:47Z","title":"Medical world models in healthcare: foundations, applications, and challenges for trustworthy clinical translation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T03:02:43.850182Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2607.25242"},"observation_digest":"sha256:51175d83712750425407ad70fa37612f5efc0b69a68507e59af71a0638164c8d","observation_id":"d2270751-b768-463b-a2c4-b77eb2b30474","resolution":{"observed_at":"2026-08-01T03:02:43.850182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-03T00:28:32.018067Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.28788","last_updated":"2026-07-30T19:27:36Z","snapshot_observed_at":"2026-08-05T23:11:44.740306Z","submitted_at":"2026-07-30T19:27:36Z","title":"EarlyDx: An Admission-Anchored Benchmark for Open-Ended Generation of Evidence-Supported ED-Encounter Diagnoses","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T00:28:32.018067Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/2607.28788"},"observation_digest":"sha256:9ec5431776aa6e8cab0b35eadc934d3483cc525e779410fc86049078a82f1bfb","observation_id":"596c238e-0567-47b4-8444-f6723061e0b9","resolution":{"observed_at":"2026-08-03T00:28:32.018067Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1904.05342/citation-record","integrity":"/paper/1904.05342/integrity","json":"/paper/1904.05342/citation-record.json","paper":"/paper/1904.05342"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1904.03323","last_updated":"2019-06-20T20:41:58Z","snapshot_observed_at":"2026-07-06T07:44:16.974916Z","submitted_at":"2019-04-06T00:34:39Z","title":"Publicly Available Clinical BERT Embeddings","version":3},"cited_work":{"arxiv_id":"1904.03323","doi":"10.48550/arxiv.1904.03323","metadata_source":"pith","pith_arxiv_id":"1904.03323","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Publicly Available Clinical BERT Embeddings","venue":"cs.CL","work_id":"f6ed1c91-cfb8-4068-b29c-322cd51c4028","year":2019},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"cited_paper":"/paper/1904.03323","citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:882d035c193ec97a6814ec473ce88762693f8f2d87143c4ccd3c140324b33a15","observation_id":"6a3ce1d3-f93c-409d-a433-07799ec200e8","resolution":{"observed_at":"2026-05-15T09:26:52.837372Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hospital readmissions in the Medicare population","venue":null,"work_id":"c3d09b9e-1843-4684-b829-abc87b868cc4","year":1984},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:39825d6d4cf669ff4423333421885d1213550d1d5c18a644855553f9297cc84c","observation_id":"a483ba6a-d55a-4947-99fc-19161ddfeb62","resolution":{"observed_at":"2026-05-15T09:26:53.032719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Aninformatics-based approach to reducing heart failure all-cause readmissions: the Stanfordheartfailuredashboard","venue":null,"work_id":"4bf32126-049b-4014-b678-468ae4704f76","year":2016},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:5085370a04d98180c04f0ca9e52bb6d39ffa4fe3ac02c49bb32ee9fec9c33d58","observation_id":"3294c92f-a590-4040-9b53-07ccc76dcff8","resolution":{"observed_at":"2026-05-15T09:26:53.036588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dynamic Hierarchical Clas- sification for Patient Risk-of-Readmission","venue":null,"work_id":"ddfd1845-2429-4019-9ed8-58a74a958fa0","year":2015},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:22264d43fe98dac3d5fd9753f70c63d8ec9751394e969b38fe2ae5c835f2156c","observation_id":"04882597-11c0-4907-84d6-9f591cb972d9","resolution":{"observed_at":"2026-05-15T09:26:53.040436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"What’s in a Note? Unpacking Predictive Value in Clinical Note Repre- sentations","venue":null,"work_id":"56347644-b909-4b41-84e5-9cf438cc7cd6","year":2018},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:4d1d1648b7016ba5f0c19dbaf289c0a4c8986ae398a9059f57d14b35c142ba03","observation_id":"6225e364-027f-4efd-9a2a-e7a56b4ac3a1","resolution":{"observed_at":"2026-05-15T09:26:52.843766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Enrich- ingwordvectorswithsubwordinformation","venue":null,"work_id":"b68998c8-3f0d-48dc-8c18-ec53c0baa1bd","year":2017},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:fd5c13853e6a86a07552be7c63ca9003ccf5ed39aa711bfc1b41ac1bf4a48e56","observation_id":"8fe4a580-4c0e-427f-918e-dae89768f9a1","resolution":{"observed_at":"2026-05-15T09:26:52.848725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Real-time prediction of mor- tality, readmission, and length of stay using electronic health record data","venue":null,"work_id":"5f695b98-7dc7-436d-9d4c-714ba0e84be4","year":2015},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:4be013913aa2f88642a39dd4d5e9c673b4535ba53a6ccffab32c4a55d7a20602","observation_id":"5e0c2841-e052-4104-a4d8-75a78e2317e8","resolution":{"observed_at":"2026-05-15T09:26:52.853779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Intelligible models for healthcare: Predicting pneu- monia risk and hospital 30-day readmission","venue":null,"work_id":"eb3854ad-14ca-4579-b698-f4aa4480f0ac","year":2015},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:3b81cbfaeb607188b3d12c869dc2076737bf1afec30a8aeffc320c3a6b49455c","observation_id":"cacf01c7-41d0-41ac-97f0-c3db38372728","resolution":{"observed_at":"2026-05-15T09:26:52.897693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for addi- tional creative solutions","venue":null,"work_id":"7cc889d2-27e9-4d27-9eff-d93bd0b95b9c","year":2011},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:179d7c0ed266cc6a2c95f48c3e0e266130b2f83823b980f21de798a20a37cc23","observation_id":"ecf9d8af-d064-48b6-a635-5691032e507c","resolution":{"observed_at":"2026-05-15T09:26:52.904539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"How to Train good Word Embeddings for Biomedical NLP","venue":null,"work_id":"dc5d5a20-3baa-49aa-b731-4144401bce39","year":2016},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:29d27a0fb257bf7f8c7962b230f15dd190188635b1f6af7c3b2db598cfe1dfa7","observation_id":"e31d3a48-1899-45a0-8c6a-a12d1f4089ae","resolution":{"observed_at":"2026-05-15T09:26:52.910130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":"1810.04805","doi":"10.1111/jofi.12885","metadata_source":"pith","pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","venue":"cs.CL","work_id":"ed240a10-5b19-406c-baa5-30803f465785","year":2018},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:b59f912ca8be976bc42e90909953f8507c5bdbf72a9ef0a61d4d56af9312bcb5","observation_id":"5079643f-2a1c-4190-a9b8-a11a22b11c2d","resolution":{"observed_at":"2026-05-15T09:26:52.814516Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A comparison of models for predicting early hospital readmissions","venue":null,"work_id":"0de318b9-8fcb-4e44-9ce9-bec1b1c1782a","year":2015},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:e9e9b6a5de283887ef1a75927d3803d10c9ee25ebfe849f224e3be4b84588fc5","observation_id":"d7b02b03-66b3-41b4-863c-f41a9fb916c0","resolution":{"observed_at":"2026-05-15T09:26:52.914612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Opportunities and challenges in developing risk pre- diction models with electronic health records data: a system- atic review","venue":null,"work_id":"8af13295-02a5-45a0-af70-c5838dd3cdd1","year":2017},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:153206333fd4f77bd2de72ac265d035a0e7bebd61211306e7f867ebe98c9dd4b","observation_id":"5a8c2f44-5e2e-41a9-8423-813aa5dbdb2d","resolution":{"observed_at":"2026-05-15T09:26:52.919886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Long Short-Term Mem- ory","venue":null,"work_id":"7ea3ab21-fda9-4e9e-a815-f39af86aa1de","year":1997},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:9d6c1e3600a36949e6cdca2c399a14981306c4676f1b41978dd2a6fdc8d21dcb","observation_id":"3afd351d-8849-4b39-8de2-db8f6c7532b8","resolution":{"observed_at":"2026-05-15T09:26:52.925018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"MIMIC-III, a freely accessible critical care database","venue":null,"work_id":"9dafbaab-d21a-47b2-a2d7-e9e0b00afe7c","year":2016},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:5c8f1f5e8d001c86904c92f4081fbc20a3137e3aff086be590232312b2198eba","observation_id":"96921dbd-a76e-4245-ba38-65f7e760ee50","resolution":{"observed_at":"2026-05-15T09:26:52.930181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Documentation of mandated discharge summary components in transitions from acute to subacute care","venue":null,"work_id":"4afb79e5-ee10-4079-92c9-0fd99599d352","year":2008},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:bf647d22ada1a73492e9d0c97e1341c20428de8adc74689aa0469f42d584e724","observation_id":"49ba999b-c103-4a12-a84f-e8c84b382017","resolution":{"observed_at":"2026-05-15T09:26:52.934826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.08746","last_updated":"2019-10-18T02:51:31Z","snapshot_observed_at":"2026-07-06T07:28:57.563261Z","submitted_at":"2019-01-25T05:57:24Z","title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","version":4},"cited_work":{"arxiv_id":"1901.08746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1901.08746","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"BioBERT: a pre-trained biomedical language repre- sentationmodelforbiomedicaltextmining","venue":null,"work_id":"4e684b3a-3a5b-48bb-b461-98435ffaf313","year":2019},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"cited_paper":"/paper/1901.08746","citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:eb061e64cfcbcd0e68c2264b7c528528da890eaf79e2adda6f52780d7285a905","observation_id":"949cd465-8716-400f-bde1-f21699836249","resolution":{"observed_at":"2026-05-15T09:26:52.822723Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep EHR: Chronic Disease Prediction Using Medical Notes","venue":null,"work_id":"dcc3d420-5174-47bb-bcdb-b2c9caf2688e","year":2018},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:f38f30faefd7d9d65b6637e19b9e6381ea39fc644d8e231d6a8dd6978d62eae8","observation_id":"249023ac-f96c-47f9-b9db-46247dff5e3d","resolution":{"observed_at":"2026-05-15T09:26:52.938675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Visualizing data using t- SNE","venue":null,"work_id":"a346b0ec-7fc5-43e4-b993-157a98f0ae15","year":2008},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:fdf8cdc2095a2866efc83dd48b873dda5df45f486c804f4b25cb20a7f7f219b2","observation_id":"4d9135b7-fde1-4abb-8d67-126a5b14d5fa","resolution":{"observed_at":"2026-05-15T09:26:52.942747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Distributed representations of words and phrases and their compositionality","venue":null,"work_id":"6792a5e6-c19b-46f1-a1bf-37534ac35b58","year":2013},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:019ded8679b6abae20b06e9859743d560766132a6f893646c568107083155efc","observation_id":"8e6ec888-f28f-4901-83cf-1d3f773fee4c","resolution":{"observed_at":"2026-05-15T09:26:52.947504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"ASHP national survey of pharmacy practice in hospital settings: Pre- scribing and transcribing—2016","venue":null,"work_id":"b4095841-12de-4138-8587-d233e180c683","year":2016},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:a0d70a9f94e591b8203640f697e9f1b30c654a7b272f5d27aad194f01a0109e1","observation_id":"035774b2-868b-49f5-b1af-19b6d069e237","resolution":{"observed_at":"2026-05-15T09:26:52.952148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Measuresofsemanticsimilarityandrelatednessinthebiomed- ical domain","venue":null,"work_id":"e14f6208-5257-4d81-a2b6-4de93a9151c3","year":2007},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:ea8cbb8a9476b8c34df1b499c9defdf0fb26e7c895d0d81ece9c0a4deca7df45","observation_id":"e80cd53f-94f4-40ee-8ca3-b9e636ec7280","resolution":{"observed_at":"2026-05-15T09:26:52.956119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Glove: Global Vectors for Word Representation","venue":null,"work_id":"e50dab95-066d-41c2-a63d-9622b113ae4e","year":2014},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:611bc60a5ad0f3b96a61a850e25b67f90c2ff02e3ae1add6012ab348caa56410","observation_id":"c276e68b-df94-409d-ba5a-4367491c1ab6","resolution":{"observed_at":"2026-05-15T09:26:52.960072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.05365","last_updated":"2018-03-22T21:59:40Z","snapshot_observed_at":"2026-07-06T06:23:28.649177Z","submitted_at":"2018-02-15T00:05:11Z","title":"Deep contextualized word representations","version":2},"cited_work":{"arxiv_id":"1802.05365","doi":"10.48550/arxiv.1802.05365","metadata_source":"pith","pith_arxiv_id":"1802.05365","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Deep contextualized word representations","venue":"cs.CL","work_id":"dd973cba-647d-49d3-9d24-061b637bb0cd","year":2018},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"cited_paper":"/paper/1802.05365","citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:9d4a262272115104a0620e89239b97babac57b00b82b57e924e215a2fa94ae6d","observation_id":"7deb6dc5-d329-4dc1-a882-ac970fb59887","resolution":{"observed_at":"2026-05-15T09:26:52.830260Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Improving Language Understanding by Gen- erative Pre-Training","venue":null,"work_id":"ead70ebc-11fe-4bdb-960a-05ead35f11f7","year":2018},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:6ef94ee9008c1177eb113be05764427ac74d6a254c4df72cb22486b3806d7163","observation_id":"cceef90a-46d2-45de-bc31-060a5a3bc94f","resolution":{"observed_at":"2026-05-15T09:26:52.964379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Scalable and accurate deep learning with electronic health records","venue":null,"work_id":"116ad55c-1801-49d2-9327-c06fdeae6696","year":2018},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:dc9285389968efaa2dc6414b511c0bc7f127146c938fa2ef4b9dbbf28ff92a2a","observation_id":"c48ed751-26c7-4ee0-b095-6248c9f6af0a","resolution":{"observed_at":"2026-05-15T09:26:52.968693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Bidirectionalrecurrentneural networks","venue":null,"work_id":"4a5d1ba8-0209-4442-81b9-802d403241dc","year":1997},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:6ba3580967ea662f81656f4c92967109deb655ac332526adabb6704dfce93134","observation_id":"c255c1f8-fdd7-4170-b40f-a95787a51575","resolution":{"observed_at":"2026-05-15T09:26:52.972494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Alarm fatigue: a patient safety concern","venue":null,"work_id":"fb1bb0d4-2403-4adc-8ffd-cfc2faf59ecd","year":2013},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:68ecc0831b379ed92b3e504fe22217f6872507bb1adce1a0d1d7d311f10d251a","observation_id":"39c8170c-b94f-47a0-a088-04646bc8d54e","resolution":{"observed_at":"2026-05-15T09:26:52.976690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neural Machine Translation of Rare Words with Subword Units","venue":null,"work_id":"73a92113-d0e6-4fe4-bcd8-6dcfd7b62a8a","year":2016},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:dac327793fd470a3efad6397a92dcdf8bd71daae1956b71a6a3c7d695a59696e","observation_id":"faf9c2c8-c311-4107-8828-750a592841ce","resolution":{"observed_at":"2026-05-15T09:26:52.980615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"DeepEHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis","venue":null,"work_id":"f0d3b56e-77e3-4b92-8729-34c3c53b5849","year":2018},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:5e146145de14ffbe2cabeb25ca416842aa3479e8a04a6570d566e1b4ab2ab660","observation_id":"8532a6bb-f493-4006-bfb0-9d95851c3ed7","resolution":{"observed_at":"2026-05-15T09:26:52.984895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Enhancing clinical concept extraction with contextual embeddings","venue":null,"work_id":"74b90767-2831-4062-80d6-ee0d7f5a0f7f","year":2019},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:7b57d9d803119f366e3bbf78fc38ebe4ae285b8ed7b97877c75610e41de55fbd","observation_id":"605c19ae-bd62-4978-ba19-0009801d3248","resolution":{"observed_at":"2026-05-15T09:26:52.988619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Ef- fect of discharge summary availability during post-discharge visits on hospital readmission","venue":null,"work_id":"d674fa80-c70f-4b2e-a788-f4e2608059c3","year":2002},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:6284334b6de4554881aac915f88197ec015b6a700092073e69a8a5892444b764","observation_id":"6f5c9e55-820e-4fd6-a80f-fa006af7c63f","resolution":{"observed_at":"2026-05-15T09:26:52.993061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Attention is all you need","venue":null,"work_id":"1db3ee2a-7831-44ee-8bc9-59aeb08a883a","year":2017},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:675c72aa6ad2f144c2db46db490592c2564c25821f49299951384b949fda38f7","observation_id":"3253eee0-03d8-4f34-9526-68629af0c8b4","resolution":{"observed_at":"2026-05-15T09:26:52.997644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A comparison of word embeddings for the biomedical natural language processing","venue":null,"work_id":"a132e5b2-5e20-40a8-bd48-47b9bbd6c602","year":2018},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:57c8542562027fcf406ff21f1b4b99c07832671edd3c9bb2fd087e50d543a304","observation_id":"98126faa-e235-44dc-b48a-800fb9542802","resolution":{"observed_at":"2026-05-15T09:26:53.003000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"MedicalSubdomainClassificationofClin- ical Notes Using a Machine Learning-Based Natural Lan- guage Processing Approach","venue":null,"work_id":"d6fde7d0-af7a-49a8-b97e-0314ee5eea43","year":2017},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:a937a7dae7b3d168ee82bb228b3652a2e5f40c9f76c198871d30269954a7f3e7","observation_id":"e607b788-2601-4bf5-a3a4-c33d46535ccf","resolution":{"observed_at":"2026-05-15T09:26:53.007827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review","venue":null,"work_id":"d571bf29-948e-4f03-87cd-3c4b1386c05f","year":2018},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:e514d3599c6da7735c7f1a275d89a033d201c5047f7283bfc7bfbd50e0bddcf4","observation_id":"e604640c-c097-4aba-a3f5-6567cc2a1d51","resolution":{"observed_at":"2026-05-15T09:26:53.012098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Artificial intelli- gence in healthcare","venue":null,"work_id":"4f1e6000-fb54-47bc-b239-40a0aacf17f4","year":2018},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:bfe82f48b0a402ed5f58d46475c5ab3f5ec0804ccdbb45965d55bcaae406673e","observation_id":"6f17333e-6990-48f0-9444-c2bffdeb6592","resolution":{"observed_at":"2026-05-15T09:26:53.016235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Understanding bag-of- words model: a statistical framework","venue":null,"work_id":"3c8fe0ab-4009-4d40-a39d-ba4c484bfc87","year":2010},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:98ea7f516ba5cded628859ea33fa1ffc88cd746a0a726858376f6fa49fc12a55","observation_id":"ae0e901a-0974-49ee-a96b-488d64c9b47d","resolution":{"observed_at":"2026-05-15T09:26:53.019781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multi-Label Learning from Medical Plain Text with Convolutional Resid- ual Models","venue":null,"work_id":"2c3207d3-5220-41f4-aaa2-69dcd1c84040","year":2018},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:3ebf0f2b05548e6840a932da38ff38f63676d195acb57569eccce5629eb5605a","observation_id":"a6c642ef-7f46-461a-a627-0cc884722b33","resolution":{"observed_at":"2026-05-15T09:26:53.024182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Readmissions, observation, and the hospital readmissions reduction program","venue":null,"work_id":"47151372-d52d-4d70-ad30-c3590a724e33","year":2016},"citing_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-15T09:26:52.783241Z"},"links":{"citing_paper":"/paper/1904.05342"},"observation_digest":"sha256:765d3565e4975b087f336a1d9c9c89c860401a95cc83ed023357000ab8b40d1a","observation_id":"221db70c-9eb6-4e08-997b-7e0ad5058c57","resolution":{"observed_at":"2026-05-15T09:26:53.028531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T07:45:18.053726Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":4,"verified_fuzzy":36},"total_outbound_references":40},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 61 inbound Pith citation observations for arXiv:1904.05342."}