{"as_of":"2026-08-19T13:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:82b0a99955a018ce1bee4ddf80eca3fc6c1ac23998315de30fc3c219d7c1bb27","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T13:48:28.943831Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/2501.16077/citation-record","integrity":"/paper/2501.16077/integrity","json":"/paper/2501.16077/citation-record.json","paper":"/paper/2501.16077"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:48:30.926526Z","title":"Snomed international","venue":null,"work_id":"1ee29419-3d6d-4cd7-903c-fc97d2889547","year":2024},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:27.874741Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:d369058f7329fcd72f72ad0d6eb1f622de7d73d1487d58c5fc6afc16f3c45f9b","observation_id":"7be86ff7-7b24-4f62-9ac4-41fd2e4372d4","resolution":{"observed_at":"2026-08-10T13:48:30.931674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T13:48:30.770260Z","title":"Cogstack-experiences of deploying integrated information retrieval and extraction services in a large national health service foundation trust hospital,","venue":null,"work_id":"5a2ed775-e045-4c83-a053-3cab37f15a61","year":2018},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:27.899948Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:f6b62ac38384eb010cead30b318cf733249b09244ffbbe8aa6285bc666045100","observation_id":"5c0ab06c-05cd-457b-ba69-e66d84f6dac2","resolution":{"observed_at":"2026-08-10T13:48:30.827175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T13:48:30.635852Z","title":"Multi-domain clinical natural language processing with medcat: the medical concept annotation toolkit,","venue":null,"work_id":"8ab71565-6422-4a10-baa7-16da2f895f7e","year":2021},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:27.924865Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:138a876f823ba13cb0bc2ca9ea7a436c1537e50526c0d2cb518c7d5dfe59bdb0","observation_id":"ea7f2935-092b-4a67-ab0b-d135da646a97","resolution":{"observed_at":"2026-08-10T13:48:30.675370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T13:48:30.571426Z","title":"Named entity recognition and relation detection for biomedical information extraction,","venue":null,"work_id":"c0fcf285-340c-4f6f-b0b7-abf6fe9f0de4","year":2020},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:27.955902Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:edb1eec50a922c57571d75361fc9de36550c0d2fde4446f66f1aecc9e8cdeebe","observation_id":"71649d2d-6d70-490b-829d-85cc6e1dcca8","resolution":{"observed_at":"2026-08-10T13:48:30.588959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T13:48:30.544980Z","title":"Understanding spatial language in radiology: Representation framework, annotation, and spatial relation extraction from chest x-ray reports using deep learning,","venue":null,"work_id":"5826754a-aa88-44b3-b117-ba45283b3e48","year":2020},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:27.981419Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:d8a8f75c203fcfa9b8cdfc26ed37e79e8ba95fdcf792df4f9b2c11fa3c20dce0","observation_id":"918fb8a2-27f4-41a9-a222-c63d46e717ca","resolution":{"observed_at":"2026-08-10T13:48:30.550481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T13:48:30.521013Z","title":"The unified medical language system (umls): integrating biomedical terminology.,","venue":null,"work_id":"8bfb282a-10bf-459c-aca8-0ac8b1acce03","year":2004},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.008465Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:beffcf4f4f9ec54bbe4d7e02e036de5ca46a3731658742c3edc7146b0637a690","observation_id":"76a8b55d-9277-449e-9a3c-53d0c7f50ade","resolution":{"observed_at":"2026-08-10T13:48:30.528407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T13:48:30.483858Z","title":"BERT: Pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":"ade7e257-0f9f-4763-be56-c601c41d88ca","year":2019},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.076539Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:42f5f9d6e719ac57fe28a14632a7334cf53157c52d4476af5a9ef854c0e306a8","observation_id":"6d02ec84-3485-45cf-84c2-a1c477870b32","resolution":{"observed_at":"2026-08-10T13:48:30.502005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-10T13:48:28.142167Z","title":"Llama: Open and efficient foundation language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.142167Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:5f3d3347bcc91c13f28b260ca460b9c473d83d7a1ab9e8ce8efece0d16ec88d6","observation_id":"47edaaf9-2a76-4a8d-9478-c4b12c814703","resolution":{"observed_at":"2026-08-10T13:48:28.142167Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:48:28.266519Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.266519Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:bbc93fce5c752fa66a8ad288957d843f25f1d09f2b47d747f20991b99de0b064","observation_id":"321a3cf4-56db-4f1a-a1bf-32a6159b624b","resolution":{"observed_at":"2026-08-10T13:48:28.266519Z","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":"2026-08-10T13:48:30.322151Z","title":"Addressing the class imbalance problem in medical datasets,","venue":null,"work_id":"b07fd358-63c0-4397-891c-0a0a3e8751f4","year":2013},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.353457Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:398acb11e6ca9f54e0e9349616738f6ac85afe4b351b021533b8a2db5b5d32bf","observation_id":"dc2f4ad2-0e46-457e-b772-642f3f8320a2","resolution":{"observed_at":"2026-08-10T13:48:30.364511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T13:48:30.294745Z","title":"What is the effect of importance weighting in deep learning?,","venue":null,"work_id":"217a9887-0586-480d-82b4-084c38bcaea6","year":2019},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.509050Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:6729af1a6a6dec5d36769012fdf873b88b9a66605f853a9931ff6a76e366fb16","observation_id":"e842a687-b90a-4f9a-9b88-fde627daa3a2","resolution":{"observed_at":"2026-08-10T13:48:30.306362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T13:48:30.184753Z","title":"Imbalanced learning: foundations, algorithms, and applications,","venue":null,"work_id":"c6d3054f-4f33-4a34-bdde-f8ab24e43692","year":2013},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.522921Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:d2ca3b345149de661d202add295b0c77ab27013129c322ef4f056a8ea9bee8c5","observation_id":"a0371364-73b8-412a-98c5-d19828da351f","resolution":{"observed_at":"2026-08-10T13:48:30.218755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T13:48:29.949705Z","title":"Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster,","venue":null,"work_id":"e3af362c-8eb1-4c64-9e96-bac2e86b7993","year":2022},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.539294Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:68b6805ca93d4f426f026340e8b1b58e2483ffbf4a8b3bbddec7ed1b2461a674","observation_id":"a7fe8caa-c4fe-45b5-97db-179c93135d07","resolution":{"observed_at":"2026-08-10T13:48:30.016361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T13:48:29.841644Z","title":"MEGA VERSE: Benchmarking large language models across languages, modalities, models and tasks,","venue":null,"work_id":"26cd26ea-6ec1-490f-8605-e724b051baba","year":2024},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.585079Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:b66f9b24fcc5005ca484b790411da8cc790709c5498d8e6b46ac17953775d1d9","observation_id":"406d0f75-1cc9-423d-8125-6b124ba4b5b8","resolution":{"observed_at":"2026-08-10T13:48:29.873249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06825","last_updated":"2023-10-10T17:54:58Z","snapshot_observed_at":"2026-08-17T20:30:34.016254Z","submitted_at":"2023-10-10T17:54:58Z","title":"Mistral 7B","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06825","snapshot_observed_at":"2026-08-10T13:48:28.625462Z","title":"Mistral 7b,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.625462Z"},"links":{"cited_paper":"/paper/2310.06825","citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:95680aa83f9e5da4a1caa13e6ebd124435c91981ec3f7c6913ac58aed3dfe914","observation_id":"3f1f3e9e-9679-4e7b-959a-edac64a42242","resolution":{"observed_at":"2026-08-10T13:48:28.625462Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:48:28.672708Z","title":"Language models are unsupervised multitask learners,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.672708Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:ae4eb1c5d53246bce6a40aaa7b366261785e45c6f6f6a346effe51557b2483c4","observation_id":"2135f2f9-9cd7-4068-80a2-ae006dab0748","resolution":{"observed_at":"2026-08-10T13:48:28.672708Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T13:48:28.714872Z","title":"Generalizing from a few examples: A survey on few-shot learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.714872Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:50dbfd9f8dd8764df4e7eac617b01cc85ed2e5b3466b465c99afe6383f94899b","observation_id":"af3cb239-9b38-497a-8599-f9050795dfa3","resolution":{"observed_at":"2026-08-10T13:48:28.714872Z","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":"2026-08-10T13:48:29.633253Z","title":"Making pre-trained language models better few-shot learners,","venue":null,"work_id":"dbba3a15-f2e0-45f0-8b0f-2b1131c78555","year":2021},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.775144Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:ff13f52e38997578cd04f8bd199ec6f068acbb129d9e517cd28eb18a87c45e85","observation_id":"2fdb28ed-6289-453f-9afc-da8b58a5d417","resolution":{"observed_at":"2026-08-10T13:48:29.675214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T13:48:29.528518Z","title":"2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records,","venue":null,"work_id":"4e22f7dc-6822-4ebc-bb95-c069de351417","year":2018},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.824152Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:9ab1aa8d9f8da8ed6d1660b32227c71a1e1a90194da271c4426491e106c8976c","observation_id":"e69db72b-c4f7-43d4-a19b-7322dd753b97","resolution":{"observed_at":"2026-08-10T13:48:29.545365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T13:48:29.445965Z","title":"2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records,","venue":null,"work_id":"a4af67b0-9647-4a97-b745-ae2c41be6d0a","year":2018},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.873175Z"},"links":{"citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:3afe158813f8bbcd62d5366ab581cdb9e18095f27c6dfc738350fa5104413d91","observation_id":"b4ef22c0-fd27-4c1f-82a9-9409d4c4b9d1","resolution":{"observed_at":"2026-08-10T13:48:29.473585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.08957","last_updated":"2021-08-16T16:03:34Z","snapshot_observed_at":"2026-08-16T18:09:02.854413Z","submitted_at":"2021-07-19T15:15:51Z","title":"Clinical Relation Extraction Using Transformer-based Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.08957","snapshot_observed_at":"2026-08-10T13:48:28.906353Z","title":"Clinical relation extraction using transformer-based models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.906353Z"},"links":{"cited_paper":"/paper/2107.08957","citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:5a3ff07273c6c926d5728b41e128375ed49237f2080b9e72fd09171c3380914a","observation_id":"ee44a08b-ee4a-41a8-9660-7f6c9d8a0ad1","resolution":{"observed_at":"2026-08-10T13:48:28.906353Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.02902","last_updated":"2021-09-08T14:19:22Z","snapshot_observed_at":"2026-08-18T00:00:34.837051Z","submitted_at":"2021-06-05T14:23:49Z","title":"BERTnesia: Investigating the capture and forgetting of knowledge in BERT","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.02902","snapshot_observed_at":"2026-08-10T13:48:28.943831Z","title":"Bertnesia: Investigating the capture and forgetting of knowledge in BERT,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T13:48:28.943831Z"},"links":{"cited_paper":"/paper/2106.02902","citing_paper":"/paper/2501.16077"},"observation_digest":"sha256:97292a4be321d8e670478ee96e4e3c2460fe4543a0e4b22450c06028e3cdde73","observation_id":"ed35551e-343d-452e-9e6c-f34265ff6843","resolution":{"observed_at":"2026-08-10T13:48:28.943831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.16077","last_updated":"2025-01-27T14:26:47Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-13T13:15:05.896749Z","submitted_at":"2025-01-27T14:26:47Z","title":"RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from Unstructured Electronic Health Records"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":15},"total_outbound_references":22},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2501.16077."}