{"as_of":"2026-08-08T07:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a0c20905bcc3598f7029c5582b1be6756fd0988ff1a31f7b2f5bde9084749f6d","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:13:19.288298Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.14079/citation-record","integrity":"/paper/2507.14079/integrity","json":"/paper/2507.14079/citation-record.json","paper":"/paper/2507.14079"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:25.363485Z","title":"Characterizing the source of text in electronic health record progress notes,","venue":null,"work_id":"afb8cd62-273c-4bb7-adb1-b6bb61a999d6","year":2017},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":1,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:16.133642Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:18e8fb4d2e5e7847dea5237bfd0a4558f9baeae858f778ddc7cfc7b501da7373","observation_id":"430f9fb9-79bf-46ff-b099-3b19a72f080c","resolution":{"observed_at":"2026-08-06T16:13:25.495632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6678.2002","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:20.099096Z","title":"Learning to write case notes using the soap format,","venue":null,"work_id":"72dcd9e9-41d2-4848-ab26-649810068cf4","year":2002},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":2,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:16.222663Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:44779d40f72a7f1916c5e39edfe6b794349bfcf1f131575372125baccb2939d6","observation_id":"118f5d6b-ad93-4c7f-804a-45e6bc9438f7","resolution":{"observed_at":"2026-08-06T16:13:20.146054Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:25.066270Z","title":"Length and redundancy of outpatient progress notes across a decade at an academic medical center,","venue":null,"work_id":"a1899d82-31b4-4da9-9e73-5847409b7559","year":2021},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":3,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:16.298913Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:6008e76d26b985d735d3068eb6b114b0c2aa5469de2838eabcd6d13d25ea6405","observation_id":"f34e2de9-f037-4f8d-89f4-28203b3c63e0","resolution":{"observed_at":"2026-08-06T16:13:25.228320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:24.794700Z","title":"Prediction of emergency department patient disposition based on natural language processing of triage notes,","venue":null,"work_id":"38e11f98-f23b-4081-882a-0a7f31aff960","year":2019},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":4,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:16.433848Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:e03aca48ce5987a7ff6dbcfef384fc38fdde41694a4d8cfaa1e2540919516955","observation_id":"0745a5be-47bd-465b-afba-09f533c9dd96","resolution":{"observed_at":"2026-08-06T16:13:24.916939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:24.573523Z","title":"Hierarchical annotation for building a suite of clinical natural language processing tasks: progress note understanding,","venue":null,"work_id":"75045057-5655-480f-afaa-8848ed02f190","year":2022},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":5,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:16.539680Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:357cca92b77b5332010279e52961f1c39fd21fcd0cc9648cf144178f6f926327","observation_id":"23ee28cd-f951-4380-b3f5-a25322fc6d98","resolution":{"observed_at":"2026-08-06T16:13:24.665368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:24.409308Z","title":"Leveraging medical knowledge graphs into large language models for diagnosis prediction: Design and application study,","venue":null,"work_id":"b8c6a825-6451-4f5e-9685-523c0db44f81","year":2025},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":6,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:16.624656Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:0d2803760a72434255c7f0f713b6192fe20efb2c94c9220bc5ecdd36929f10d8","observation_id":"64324df0-bc11-4499-ab93-10ddb6401647","resolution":{"observed_at":"2026-08-06T16:13:24.474793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:24.250717Z","title":"Attention-based clinical note summarization,","venue":null,"work_id":"4dba2326-b46e-4d77-af20-a868a9a0cecf","year":2022},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":7,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:16.769609Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:393626f0c950d73fa667364e4a8fdf01de7e836a26762b2c6359a8b4e1645b25","observation_id":"6ed64f41-2d5e-43e1-b493-99e6f54e003a","resolution":{"observed_at":"2026-08-06T16:13:24.315555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:24.132273Z","title":"A multimodal transformer: Fusing clinical notes with structured ehr data for interpretable in-hospital mortality prediction,","venue":null,"work_id":"247e7dfb-b0f6-4c6b-9998-b1596a51a632","year":2022},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":8,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:16.842460Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:174991ad44fb2e91bcac652b80d2884296ec881e65aeade4f30b44143802833f","observation_id":"ab16b021-66ca-4c41-8e83-4197677deded","resolution":{"observed_at":"2026-08-06T16:13:24.179616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:23.980690Z","title":"Reducing redundancy in clinical documentation: a study of progress note content and structure,","venue":null,"work_id":"86afb8cb-3192-40ee-b915-ec6f9d6a4d5d","year":2024},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":9,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:16.935668Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:82b1dbf684121cf4b5ef9ae24063852b0c29b7c014f7ec639fde360bb0c2c30f","observation_id":"50004c0a-db05-4a45-8e61-eeec0cedbb61","resolution":{"observed_at":"2026-08-06T16:13:24.053515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:23.843144Z","title":"Copy, paste, and cloned notes in electronic health records,","venue":null,"work_id":"9d950e45-f2bd-4535-b509-68c53bde8c6e","year":2014},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":10,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:17.011791Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:735fec0f23f603f60f824759311eafb4ff9e28e901f2c9c8cfdf8b7e13951ddb","observation_id":"d6b2518d-e146-4efd-af4a-e632c8bcdc82","resolution":{"observed_at":"2026-08-06T16:13:23.893226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:23.681161Z","title":"Clinical documentation in the 21st century: executive summary of a policy position paper from the american college of physicians,","venue":null,"work_id":"2d8809e2-5d57-419a-a2ca-28fadb627c78","year":2015},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":11,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:17.116590Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:bc56a6a383d2ef09a6e01deaa0e3908e7c226390b7297a839e47de6b0a279255","observation_id":"fc8c773d-9b32-4502-a6d7-6eb6c3d18adf","resolution":{"observed_at":"2026-08-06T16:13:23.740006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:17.165721Z","title":"Mimic-iii, a freely accessible critical care database,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":12,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:17.165721Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:d825267d82c9dbb7696f3a208374cd01a49d21e7799f928b8e046dbf4ed64dd7","observation_id":"b8671252-92f5-4f6c-b368-6562f8424c9f","resolution":{"observed_at":"2026-08-06T16:13:17.165721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:23.567083Z","title":"Redundancy of progress notes for serial office visits,","venue":null,"work_id":"e468a451-1bd8-42b6-94c4-5a8ae16fdb93","year":2020},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":13,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:17.275579Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:1a48aa82da8a7748222de7bf97c3b484a1c7f6c5c8ef59fa55a3b71fb2ff71ad","observation_id":"9a7d0f15-2264-4cc7-b530-3af122483c7c","resolution":{"observed_at":"2026-08-06T16:13:23.611899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.06715","last_updated":"2025-07-09T10:13:38Z","snapshot_observed_at":"2026-08-06T18:54:07.850409Z","submitted_at":"2025-07-09T10:13:38Z","title":"CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.06715","snapshot_observed_at":"2026-08-06T16:13:17.389506Z","title":"Cli-rag: A retrieval-augmented framework for clinically structured and context aware text generation with llms,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":14,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:17.389506Z"},"links":{"cited_paper":"/paper/2507.06715","citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:802c07a5aba063ef5d5a5bd9b276f16434c60ee6e73dfbaf65fd38c28aebe6a6","observation_id":"1f2408b2-1651-49c3-8d2c-19610d3b223b","resolution":{"observed_at":"2026-08-06T16:13:17.389506Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:22.915620Z","title":"Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition,","venue":null,"work_id":"7c2564e5-7e8d-42a0-a39b-8e7b69a029f2","year":2021},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":15,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:17.488379Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:2a583110f29d3dae9706146bc6954ee7c08ca8da711ef224d96dfc0c339a1853","observation_id":"94c49fc7-4381-4f32-a536-486c02e1eea6","resolution":{"observed_at":"2026-08-06T16:13:23.334397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.07002","last_updated":"2019-05-21T19:36:35Z","snapshot_observed_at":"2026-08-06T01:58:37.531653Z","submitted_at":"2019-05-16T19:14:18Z","title":"Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models","version":2},"cited_work":{"arxiv_id":"1905.07002","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.07002","snapshot_observed_at":"2026-08-06T16:13:19.932302Z","title":"Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models","venue":"cs.CL","work_id":"fc815050-fa8a-48bb-87d1-edf8ba3df687","year":2019},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":16,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:17.571290Z"},"links":{"cited_paper":"/paper/1905.07002","citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:f09c1ad27ba8ade059fdf3a0bac996d36c073c3cb128a378381377d569f151e5","observation_id":"6141f284-bee4-4b9c-a94b-0da02ae9df6e","resolution":{"observed_at":"2026-08-06T16:13:19.956241Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05786","last_updated":"2019-10-24T16:20:06Z","snapshot_observed_at":"2026-07-06T08:29:07.404994Z","submitted_at":"2019-10-13T16:54:21Z","title":"Progress Notes Classification and Keyword Extraction using Attention-based Deep Learning Models with BERT","version":2},"cited_work":{"arxiv_id":"1910.05786","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.05786","snapshot_observed_at":"2026-08-06T16:13:19.774406Z","title":"Progress Notes Classification and Keyword Extraction using Attention-based Deep Learning Models with BERT","venue":"cs.CL","work_id":"96b0202b-ff23-4ca7-be7c-2b61ee603ba1","year":2019},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":17,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:17.760040Z"},"links":{"cited_paper":"/paper/1910.05786","citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:85a4ed3591f44d09dc0669f1c08e53079b0f2fbf1380defe3f203fa938250037","observation_id":"f76e92c7-4f6d-4aad-b92d-0df98de95f97","resolution":{"observed_at":"2026-08-06T16:13:19.831236Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:22.254881Z","title":"Toward relieving clinician burden by automatically generating progress notes using interim hospital data,","venue":null,"work_id":"691dd84d-0479-4700-ba70-665d6c1c2b8d","year":2024},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":18,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:17.827091Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:3d31f1df00f6065e345ad4074c2b9afe6ecb7036f111cc59e4d08e34d2e78804","observation_id":"e67969b7-729a-467e-8dbe-9e063948dcad","resolution":{"observed_at":"2026-08-06T16:13:22.555669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.18346","last_updated":"2024-05-28T16:43:41Z","snapshot_observed_at":"2026-08-06T20:06:18.397322Z","submitted_at":"2024-05-28T16:43:41Z","title":"Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.18346","snapshot_observed_at":"2026-08-06T16:13:17.650376Z","title":"Intelligent clinical documentation: Har- nessing generative ai for patient-centric clinical note generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":19,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:17.650376Z"},"links":{"cited_paper":"/paper/2405.18346","citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:600c51cec0e4e2e16282762379c876ccb42b981319d66955aa7a4a44c6ecb9fb","observation_id":"bf7c0d93-e9a7-4aea-81d2-b16d9303f7dd","resolution":{"observed_at":"2026-08-06T16:13:17.650376Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:21.976696Z","title":"Clinicalt5: A generative language model for clinical text,","venue":null,"work_id":"3754c1d8-be44-40a2-be27-a85ca47fec67","year":2022},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":20,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:17.922527Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:99ce6bbe681aa2e104cc99aff958ab21bf9fd3507fadde0eed223f142fd7115b","observation_id":"d77ff64e-b725-4b45-86b0-aecea346c057","resolution":{"observed_at":"2026-08-06T16:13:22.067974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:21.858017Z","title":"Assessing electronic note quality using the physician documentation quality instru- ment (pdqi-9),","venue":null,"work_id":"4ae2d46f-ec19-48c2-813e-5d7cd63f881c","year":2012},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":21,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:17.997055Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:8aacc6435e51a717db1902bfc134f5434889899594b450ab43a478f80f7b3237","observation_id":"75087547-b3ad-488c-8586-dd4b3ececd7f","resolution":{"observed_at":"2026-08-06T16:13:21.892243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:21.775847Z","title":"Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record,","venue":null,"work_id":"843fc9dd-aae9-4128-ac15-9c3a8630834c","year":2018},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":22,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:18.072090Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:3ce32961724dcb5ec9cbd8514636e332489798f802257919a5ad89098840bc1e","observation_id":"bd835e87-2033-4658-9607-3ab0c4eaed63","resolution":{"observed_at":"2026-08-06T16:13:21.815509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:21.628675Z","title":"Advancing informatics with electronic medical records bots (emrbots),","venue":null,"work_id":"23b88db0-aa00-4465-a43e-24fde7a798b6","year":2019},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":23,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:18.088023Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:0142e3490b65ed62641f47cbe1af822ceb68591ddb970c6233d18a99a9071112","observation_id":"51db5334-e370-4353-845e-6e1a456d27ed","resolution":{"observed_at":"2026-08-06T16:13:21.688746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:13:18.248401Z","title":"Gen- erating multi-label discrete patient records using generative adversarial networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":24,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:18.248401Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:3a5459d2ed66005c217f5d30f7b3a5e51d1798e533dacb43799edc5bd8364c55","observation_id":"3d600dba-43cd-40cd-a6dc-a20b54c02207","resolution":{"observed_at":"2026-08-06T16:13:18.248401Z","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":"2026-08-06T20:06:43.553572Z","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":"sha256:3ebc43cbebd2635770c76acc092581fb71b6b28ebd65624b64701e73ea143ea4","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":"2309.00237","last_updated":"2024-07-29T15:52:22Z","snapshot_observed_at":"2026-07-06T16:13:07.384791Z","submitted_at":"2023-09-01T04:01:20Z","title":"Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes","version":4},"cited_work":{"arxiv_id":"2309.00237","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.00237","snapshot_observed_at":"2026-08-06T16:13:19.618741Z","title":"Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes","venue":"cs.CL","work_id":"6ba19d9b-92d6-4945-b118-0b0d464808e9","year":2023},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":26,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:18.506551Z"},"links":{"cited_paper":"/paper/2309.00237","citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:b8967029a51ff40fa6cb0661441c68c650f20b1a5dc38830fc0069605701eda4","observation_id":"fcd89506-655f-415b-a5ce-29cf16720bc6","resolution":{"observed_at":"2026-08-06T16:13:19.672387Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.05545","last_updated":"2019-10-10T22:44:28Z","snapshot_observed_at":"2026-07-06T08:07:15.233485Z","submitted_at":"2019-07-12T01:55:36Z","title":"The Dynamic Embedded Topic Model","version":2},"cited_work":{"arxiv_id":"1907.05545","doi":null,"metadata_source":"pith","pith_arxiv_id":"1907.05545","snapshot_observed_at":"2026-08-06T16:13:19.538370Z","title":"The Dynamic Embedded Topic Model","venue":"cs.CL","work_id":"7c51d6bc-2909-46db-9c47-131f57ea0349","year":2019},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":27,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:18.660354Z"},"links":{"cited_paper":"/paper/1907.05545","citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:304990fd0e1ef8843922ca89d4999e9284e449a0edf499e79730fe21cec3b882","observation_id":"01e29cfd-c2ff-4072-afd4-82685f466bc4","resolution":{"observed_at":"2026-08-06T16:13:19.579687Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:21.454288Z","title":"Etm: Enrichment by topic modeling for automated clin- ical sentence classification to detect patients’ disease history,","venue":null,"work_id":"acf193bc-5a7f-4b12-b939-4d7241957dbd","year":2020},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":28,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:18.820436Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:d7b8ea1b61eac886e18cb5397716a6f6a0641bb38612b484943129dc97b137fe","observation_id":"b1f210e8-46aa-4037-9e6e-309c5c1315d1","resolution":{"observed_at":"2026-08-06T16:13:21.482413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:21.269622Z","title":"Dynamic topic models,","venue":null,"work_id":"5f1ec31a-4ba1-4a28-8ae9-b64a4ed9a856","year":2006},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":29,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:18.903158Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:c64c878fdda4ee0f72e006f2405596927d8503b76ef9c3ff9e472c17510ad724","observation_id":"b10fdac9-effe-4fee-a266-62abf8e0a62a","resolution":{"observed_at":"2026-08-06T16:13:21.350465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:21.066390Z","title":"A systematic review of large language model (llm) evaluations in clinical medicine,","venue":null,"work_id":"bd95be8d-4cfd-4f32-ac05-20a38aeef909","year":2025},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":30,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:18.971519Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:3163c382f9768eb2416b47044e3488378596ba3123a1a34f2c24de2dfd775dfb","observation_id":"6961c0b8-4e68-48fc-b35f-2deaabe679a8","resolution":{"observed_at":"2026-08-06T16:13:21.174911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:20.852871Z","title":"Evaluating measures of redundancy in clinical texts,","venue":null,"work_id":"a84b8d09-e663-40a7-b74f-26ab26bd27a8","year":2011},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":31,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:19.033560Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:da07c194f99860fcba6bd7a6a65fcb2438e24b930861569510e8a82bd3babd80","observation_id":"c4a05adf-ff83-455b-b2cf-d4261111c004","resolution":{"observed_at":"2026-08-06T16:13:20.960282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:20.643391Z","title":"Quantifying clinical narrative redundancy in an electronic health record,","venue":null,"work_id":"5b21d7b4-61a6-466e-b49f-8fa40232d287","year":2010},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":32,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:19.095946Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:dbb8f76ad5d8852b480fcdcaa8e1dc62594a1ff641cf4145c1d6a25864999954","observation_id":"d57b0b56-13e3-4fed-b1ff-3ba415d00e4d","resolution":{"observed_at":"2026-08-06T16:13:20.761145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:20.433319Z","title":"“note bloat","venue":null,"work_id":"5192bc17-a29b-4cf8-9422-6675acf0bcf7","year":2022},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":33,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:19.141158Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:6f85a744302d4b3bde1ade14c63682ce8be866607fbb47307acd62bae5b487a7","observation_id":"f643500a-6f3a-4eb6-b5a1-c676f63b7caa","resolution":{"observed_at":"2026-08-06T16:13:20.530702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:20.316806Z","title":"Modeling local coherence: An entity-based approach,","venue":null,"work_id":"938361c0-2c26-4f52-afb5-9f50383b7eee","year":2008},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":34,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:19.202345Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:a7e00f2f66170995f0cf91fc44e9a3683abe4d95483f5a2025f3619b812fa4d1","observation_id":"d3fbfb06-1d65-4c06-9b78-595afd14d31c","resolution":{"observed_at":"2026-08-06T16:13:20.368312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T16:13:20.191619Z","title":"Neural net models of open-domain discourse coherence,","venue":null,"work_id":"8905bbf3-b003-4767-871c-2f3b3854fad0","year":2017},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":35,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:19.248942Z"},"links":{"citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:d99f9aa316e3b1dc36c04abb94d8735ad915e32e5a71429690adaf90c12550d3","observation_id":"bd33d576-c72b-44c4-bd23-e2f3bed494d0","resolution":{"observed_at":"2026-08-06T16:13:20.246180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.17047","last_updated":"2025-05-15T16:14:53Z","snapshot_observed_at":"2026-08-07T15:45:00.927190Z","submitted_at":"2025-05-15T16:14:53Z","title":"Assessing the Quality of AI-Generated Clinical Notes: A Validated Evaluation of a Large Language Model Scribe","version":1},"cited_work":{"arxiv_id":"2505.17047","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.17047","snapshot_observed_at":"2026-08-06T16:13:19.408857Z","title":"Assessing the Quality of AI-Generated Clinical Notes: A Validated Evaluation of a Large Language Model Scribe","venue":"cs.CL","work_id":"9e4c80e2-9326-4029-881a-56c3ce01fd24","year":2025},"citing_paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","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":36,"source":"pdf_text","source_observed_at":"2026-08-06T16:13:19.288298Z"},"links":{"cited_paper":"/paper/2505.17047","citing_paper":"/paper/2507.14079"},"observation_digest":"sha256:b07604144efd6a532ed25072f4d96409e78426bde012900e35d737042ea4d03b","observation_id":"6ef2f7f2-3e54-4282-9a1e-41f6c859800b","resolution":{"observed_at":"2026-08-06T16:13:19.463456Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.14079","last_updated":"2025-07-18T17:00:27Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-06T20:06:43.553572Z","submitted_at":"2025-07-18T17:00:27Z","title":"DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":6,"verified_fuzzy":25},"total_outbound_references":36},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.14079."}