{"as_of":"2026-08-21T21:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2e0d0d3705f70fbf95593f91a97afefcc53e638697304c0713ae7c7329607090","coverage":[{"denominator":83,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":83,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T11:38:51.299205Z","state":"measured"},{"denominator":83,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":83,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/2510.03844/citation-record","integrity":"/paper/2510.03844/integrity","json":"/paper/2510.03844/citation-record.json","paper":"/paper/2510.03844"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T11:38:43.785022Z","title":"Implementing the learning health system: from concept to action","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:43.785022Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:f50feb6b1cd9c12e5c32410cec40e4a5aa89b1bf8a3afece4ad5103b870879ac","observation_id":"87a8230f-e210-4de7-9c11-c9ea8b4bc890","resolution":{"observed_at":"2026-08-04T11:38:43.785022Z","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":"2026-08-04T11:38:43.820109Z","title":"Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:43.820109Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:3c537fef7a1e03ca33add83e2677e2f721cf906bea3a455e04f4bd24165672e5","observation_id":"921967eb-60dc-45fa-9549-0f5a26fd90bb","resolution":{"observed_at":"2026-08-04T11:38:43.820109Z","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":"2026-08-04T11:38:43.913505Z","title":"Toward a science of learning systems: A research agenda for the high-functioning learning health system","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:43.913505Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:93a7b0d6dad5f946f8ccc084b0de3911da360eeff7305c2c1fbadce979cfe7ce","observation_id":"28e21957-1e1b-4576-9ed2-01fd56dbdd95","resolution":{"observed_at":"2026-08-04T11:38:43.913505Z","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":"2026-08-04T11:38:44.008123Z","title":"A framework for analysing learning health systems: Are we removing the most impactful barriers? LHS","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:44.008123Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:c1fa68a7c5ace5fa716bb1a0730ae863b15ba67646fed47b16e1f856ee5783ad","observation_id":"4ca0145b-81dc-45ac-a305-841c318e4db3","resolution":{"observed_at":"2026-08-04T11:38:44.008123Z","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":"2026-08-04T11:38:44.066187Z","title":"Applying implementation science to advance electronic health record-driven learning health systems: Case studies, challenges, and recommendations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:44.066187Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:489ef885a167fcc33e3ad846a87b3d6126d6b9fb8330404a113f5e00f378f5b5","observation_id":"f8ae7c07-0181-42aa-a8e3-6c81490d4331","resolution":{"observed_at":"2026-08-04T11:38:44.066187Z","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":"2026-08-04T11:38:44.114102Z","title":"Desiderata for computable representa- tions of electronic health records-driven phenotype algorithms","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:44.114102Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:5789d631090f1eae701b389a6cf83b3dc12ee0d97969e15e48c0428423d898a7","observation_id":"ec78f7ee-398c-40a2-9cd6-2435718d7b53","resolution":{"observed_at":"2026-08-04T11:38:44.114102Z","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":"2026-08-04T11:38:44.184679Z","title":"Caveats for the use of operational electronic health record data in comparative effectiveness research","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:44.184679Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:3a22b170753b463c5bd31ca932c7ca27ff1e7280fcc5184c6102b4b6d2db471a","observation_id":"d5b8a68a-6917-4520-a07a-60b970427417","resolution":{"observed_at":"2026-08-04T11:38:44.184679Z","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":"2026-08-04T11:38:44.356156Z","title":"The evolving use of electronic health records (EHR) for research","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:44.356156Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:25b6bc50a47c51fdfcf729071503065167a2835ebcd08c5b59b9c9c73d82dfee","observation_id":"123dff1e-0434-4e4b-8e6d-c66b4d675065","resolution":{"observed_at":"2026-08-04T11:38:44.356156Z","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":"2026-08-04T11:38:44.454925Z","title":"Use of EHRs data for clinical research: Historical progress and current applications","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:44.454925Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:703a1c7d38a5adb4ae007163510d11798b45809578bd9278c7fbb484c406d140","observation_id":"7dd68f12-0f6d-48df-aebc-abb21a162d97","resolution":{"observed_at":"2026-08-04T11:38:44.454925Z","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":"2026-08-04T11:38:44.542169Z","title":"Challenges in and opportunities for rlectronic health record-based data analysis and interpretation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:44.542169Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:ee5f1b3bf5ddb5b16586b8d7e21876e4631a9afc8a7c93e27f4cd25aec4283fa","observation_id":"24182774-9db6-4221-9f35-9a2fa11d4451","resolution":{"observed_at":"2026-08-04T11:38:44.542169Z","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":"2026-08-04T11:38:44.623010Z","title":"Assessing missing data assumptions in EHR-based studies: A complex and underappreciated task","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:44.623010Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:b75ccbdb2f337a1aaef29c4ca6276aa0ca70f9361c3f09145b5c426b9400d9da","observation_id":"50507b71-372c-49ce-adae-97e66646a3fa","resolution":{"observed_at":"2026-08-04T11:38:44.623010Z","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":"2026-08-04T11:38:44.669852Z","title":"Mining for equitable health: Assessing the impact of missing data in electronic health records","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:44.669852Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:79008a0b5e08b58d246bc4c9d862d08ffaa5e818f1fb94efac71ece79cc2c0c4","observation_id":"10666cd3-e948-444a-af11-37dd92fa54d4","resolution":{"observed_at":"2026-08-04T11:38:44.669852Z","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":"2026-08-04T11:38:44.765338Z","title":"The concept of allostasis in biology and biomedicine","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:44.765338Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:08d08d0d6be0086438952c8c6f9b982a9d589b4a2420605507ca03546560bb07","observation_id":"344a0fe9-6235-44e4-8d3a-54a49ce54848","resolution":{"observed_at":"2026-08-04T11:38:44.765338Z","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":"2026-08-04T11:38:44.829574Z","title":"Allostasis: A new paradigm to explain arousal pathology","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:44.829574Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:fe3a545f3353e17a1fa1c8f1d70ea5e45876c2a34d32c788055afa1b9eb35838","observation_id":"e2913c4b-abff-4905-ba32-6c6d135906ad","resolution":{"observed_at":"2026-08-04T11:38:44.829574Z","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":"2026-08-04T11:38:44.911649Z","title":"Stress and the individual","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:44.911649Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:c9ad0347c35ee6a623038a2588f47f495d60e04c480b98c4893a8679d5bfa9e4","observation_id":"3d14c5c2-cd50-47e2-b6e5-58dcd7d8dd44","resolution":{"observed_at":"2026-08-04T11:38:44.911649Z","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":"2026-08-04T11:38:44.974398Z","title":"What is in a name? Integrating homeostasis, allostasis and stress","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:44.974398Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:8c8133220b8001e075d57d2490fd7bdabd0ff3a50fc936c8e9657a56bb16137e","observation_id":"89a6ada3-ee08-4ebf-b55c-76bdb482e8a6","resolution":{"observed_at":"2026-08-04T11:38:44.974398Z","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":"2026-08-04T11:38:45.072137Z","title":"A systematic review of allostatic load, health, and health disparities","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:45.072137Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:9cb225db88d38985a952557d01a7770858b713292d97b2a94ef85cd69a8cee68","observation_id":"42f2f2bd-9637-433e-9a1d-a7ff5de636fe","resolution":{"observed_at":"2026-08-04T11:38:45.072137Z","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":"2026-08-04T11:38:45.175601Z","title":"Allostatic load burden and racial disparities in mortality","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:45.175601Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:80346821be016501368177fe68aa1c5225d5730413b7b4cbf310234825fe44be","observation_id":"1aeaea47-ba44-4bd4-9da0-b90fbff22feb","resolution":{"observed_at":"2026-08-04T11:38:45.175601Z","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":"2026-08-04T11:38:45.246777Z","title":"Combinations of biomarkers predictive of later life mortality","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:45.246777Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:5b5e1308be10fcc9b5445979b603ce6d5280e27d079c962e693634d488ab9aac","observation_id":"44c27f31-b211-477a-b714-757dae1420a3","resolution":{"observed_at":"2026-08-04T11:38:45.246777Z","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":"2026-08-04T11:38:45.304221Z","title":"Allostatic overload in patients with essential hypertension","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:45.304221Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:5c9be80c5f108ea6621481ec2eb0829daaf84d23bebc3e08789f7f4b62d07279","observation_id":"dc431d47-a04e-429c-9555-925684e44144","resolution":{"observed_at":"2026-08-04T11:38:45.304221Z","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":"2026-08-04T11:38:45.381763Z","title":"Allostatic Load and Its Impact on Health: A Systematic Review","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:45.381763Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:7e590fea6632d36065d5fd6117e0c052599a2d5e34ef453f477c52a082bdf570","observation_id":"f3551e10-feef-4693-bd00-1cc57d6e51fd","resolution":{"observed_at":"2026-08-04T11:38:45.381763Z","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":"2026-08-04T11:38:45.492652Z","title":"Stress, adaptation, and disease","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:45.492652Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:152f6392808244f454aa3b6a0861ce73d0beefc80df418f8ab28b609161ea691","observation_id":"51377e38-9337-4c15-a0ff-96ca65731be0","resolution":{"observed_at":"2026-08-04T11:38:45.492652Z","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":"2026-08-04T11:38:45.611642Z","title":"Allostatic load and mortality: A systematic review and meta-analysis","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:45.611642Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:7b93dbaa0224d2cfd3917ba3415aa3b28cb8dd2ca58adc5de4fefab61f2378f2","observation_id":"896aa589-96c6-4767-bd8f-ab6d91984e32","resolution":{"observed_at":"2026-08-04T11:38:45.611642Z","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":"2026-08-04T11:38:45.721688Z","title":"Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:45.721688Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:b37d11592cc33486a5462893cefb9c7282b40606c56ef4d419907bbe7d1271c8","observation_id":"94969ee0-ecd4-4dbf-ae31-3b85703c8832","resolution":{"observed_at":"2026-08-04T11:38:45.721688Z","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":"2026-08-04T11:38:45.831583Z","title":"Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:45.831583Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:7d46b1324042133f757771e0e9f0e55548317ebf8787f3dd876453082f61ddda","observation_id":"24591ace-5cc5-487b-a501-56e9073e6543","resolution":{"observed_at":"2026-08-04T11:38:45.831583Z","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":"2026-08-04T11:38:45.905259Z","title":"Quality of data collection in a large HIV observational clinic database in sub-Saharan Africa: implications for clinical research and audit of care","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:45.905259Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:f2e67a0745ef839f57c14f7305e6be32dee5b40c233b163d19ddaa821850b7cb","observation_id":"dfe53ec5-41e5-4d95-9ecb-52230779de13","resolution":{"observed_at":"2026-08-04T11:38:45.905259Z","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":"2026-08-04T11:38:45.964100Z","title":"Improving public health information: A data quality intervention in KwaZulu-Natal, South Africa","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:45.964100Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:045198ab98168b89d3ed67b13bc16f8d142cd3a258a9ef47817bea22016f4732","observation_id":"bd497eea-949a-46bd-8451-730bb738eadb","resolution":{"observed_at":"2026-08-04T11:38:45.964100Z","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":"2026-08-04T11:38:46.062342Z","title":"Design and implementation of a health management information system in Malawi: Issues, innovations and results","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:46.062342Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:64ca6d7f3fbd71fe6c2027e72f294f715787bdeb862ddc1a5628fd6a786631a3","observation_id":"84f5ae1f-2740-4987-84ef-0648661ce03f","resolution":{"observed_at":"2026-08-04T11:38:46.062342Z","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":"2026-08-04T11:38:46.207559Z","title":"Analysing the hindrance to the use of information and technology for improving efficiency of health care delivery system in Tanzania","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:46.207559Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:8b49eb0c8fae7394ef1051db448c7621b7349a39e532e26561a1059a8f6bbdf3","observation_id":"6cfcf8b1-f040-4913-b7c8-2b9384b8764b","resolution":{"observed_at":"2026-08-04T11:38:46.207559Z","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":"2026-08-04T11:38:46.296208Z","title":"Multiwave validation sampling for error-prone electronic health records","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:46.296208Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:4ffbf5cfa0a145cef21aacc5e9a8aadb92936271c44a1b2d9cf65e8cd3cbabe1","observation_id":"f365c414-abcc-4c50-ba4e-1dd4fa262dbb","resolution":{"observed_at":"2026-08-04T11:38:46.296208Z","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":"2026-08-04T11:38:46.370114Z","title":"Measuring the quality of observational study data in an international HIV research network","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:46.370114Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:0836a5e721e4b188930e67584afd911f178363a444419d5e5372dfaa06445be1","observation_id":"affdd99a-7161-42d2-a55b-849e6e022ff7","resolution":{"observed_at":"2026-08-04T11:38:46.370114Z","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":"2026-08-04T11:38:46.510080Z","title":"Self-audits as alternatives to travel- audits for improving data quality in the Caribbean, Central and South America network for HIV epidemiology","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:46.510080Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:6b925f5a01f07ce65fc36d42ac4c5e482bc811a0172a1ab8126fc01385538d4f","observation_id":"68fd3b7c-eb51-4e99-bf83-b89b94c7f810","resolution":{"observed_at":"2026-08-04T11:38:46.510080Z","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":"2026-08-04T11:38:46.643908Z","title":"Lessons learned from over a decade of data audits in international observational HIV cohorts in Latin America and East Africa","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:46.643908Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:a4fc96b3e38329fe4af38f734adfa3ac0bb6b7d97509d7d9a4f41d758688b3b7","observation_id":"f10932fd-d2c3-493f-b6c9-22fb57575474","resolution":{"observed_at":"2026-08-04T11:38:46.643908Z","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":"2026-08-04T11:38:46.757693Z","title":"Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:46.757693Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:da4479c0c9460c891e691f593f7b515eca133359ee83c211fc45b9d32eaa6efd","observation_id":"78911ceb-4392-46c8-9957-9693bf92c02e","resolution":{"observed_at":"2026-08-04T11:38:46.757693Z","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":"2026-08-04T11:38:46.856254Z","title":"Using Anchors to Estimate Clinical State without Labeled Data","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:46.856254Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:996176761510462bc6146f71527506cb77247632a9cf959dadd2c2a4a640aa20","observation_id":"3e1fd9ec-f54f-4536-a4f0-16180f979865","resolution":{"observed_at":"2026-08-04T11:38:46.856254Z","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":"2026-08-04T11:38:46.953284Z","title":"International Statistical Classification of Diseases and Related Health Problems: Tenth Revision","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:46.953284Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:60967b6bfa4e546c4688be9078ae377881656080e2aa80ead3b826babf4b2db3","observation_id":"318e074b-4eb9-49ef-a5a8-78196d786573","resolution":{"observed_at":"2026-08-04T11:38:46.953284Z","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":"2026-08-04T11:38:47.115078Z","title":"Using electronic health records to generate phenotypes for research","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:47.115078Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:fb91a6d808ff04eed3fe86040bcb70dd268dd1c69eb5dff67e77e221f58dda31","observation_id":"b5784863-c990-43e5-86f7-a9b8156c9cea","resolution":{"observed_at":"2026-08-04T11:38:47.115078Z","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":"2026-08-04T11:38:47.294158Z","title":"High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:47.294158Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:52ac460c355f041709dea13ed79ef0d3ac501f6123945ea7d9ce0e5445c6e482","observation_id":"20d26402-16d9-4368-aa9d-8775398324fb","resolution":{"observed_at":"2026-08-04T11:38:47.294158Z","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":"2026-08-04T11:38:47.428158Z","title":"Machine learning approaches for electronic health records phenotyping: A methodical review","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:47.428158Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:d265f5e9f848837d89bf7f61decb1151806fe5ac689620486a12f0d45cc411d7","observation_id":"4e96bcc9-40d0-4133-b02b-1e199c366a5b","resolution":{"observed_at":"2026-08-04T11:38:47.428158Z","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":"2026-08-04T11:38:47.496962Z","title":"The validation of electronic health records in accurately identifying patients eligible for colorectal cancer screening in safety net clinics","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:47.496962Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:14ea1de52a6d8257390877420e0e6a0f37d7618265563123465d952b9563507d","observation_id":"149bc324-af56-417c-b24f-93ed6e6ec8bf","resolution":{"observed_at":"2026-08-04T11:38:47.496962Z","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":"2026-08-04T11:38:47.578226Z","title":"Toward a computable phenotype for determining eligibility of lung cancer screening using electronic health records","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:47.578226Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:ec005181f59bd70435b8eda101c3f741ba0e7377b819a66a8d709b176865cfc9","observation_id":"1fcf21a5-221c-4d76-aad7-8251510a51d4","resolution":{"observed_at":"2026-08-04T11:38:47.578226Z","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":"2026-08-04T11:38:47.676106Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:47.676106Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:891fbc87084761df1732d870f15ec2fb4029febc1b5b785b23ab8c12f4d6207b","observation_id":"8ae225e9-19a2-4e4b-a0e5-864ed10faa0a","resolution":{"observed_at":"2026-08-04T11:38:47.676106Z","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":"2026-08-04T11:38:47.737713Z","title":"Leveraging natural language process- ing to identify eligible lung cancer screening patients with the electronic health record","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:47.737713Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:d98c4dba2eee945d33e9cebc51a61c81740cbcdd41e842a733fe880043be015e","observation_id":"cd55309d-b90e-45a6-8adb-3cb6a6037056","resolution":{"observed_at":"2026-08-04T11:38:47.737713Z","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":"2026-08-04T11:38:47.774600Z","title":"Develop and validate a computable phenotype for the identification of Alzheimer’s disease patients using electronic health record data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:47.774600Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:f1bd37f6aef572126876036e569416aab5aa10a9aab2392763f6a614208f44b7","observation_id":"ba232ffb-ce29-4a2e-8bf2-0b79199671c7","resolution":{"observed_at":"2026-08-04T11:38:47.774600Z","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":"2026-08-04T11:38:47.834788Z","title":"Development and validation of eRADAR: A tool using EHR data to detect unrecognized dementia","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:47.834788Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:955f12bb98bb49892b39677a1f62c5957e25ec692796c6f6609e71178f96dc64","observation_id":"749285e0-bc89-4f41-86ee-d4247b8807e2","resolution":{"observed_at":"2026-08-04T11:38:47.834788Z","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":"2026-08-04T11:38:47.907332Z","title":"Evaluation of an algorithm for identifying ocular conditions in electronic health record data","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:47.907332Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:410c9f2c25c8e6acca0cb4e180ac9d2c625dfdfa2fdf5d6ac8c579869a0b2cdd","observation_id":"8cadcfac-c7ab-49c0-be13-eb48c24b4d61","resolution":{"observed_at":"2026-08-04T11:38:47.907332Z","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":"2026-08-04T11:38:47.976539Z","title":"Identifying lupus patients in electronic health records: Development and validation of machine learning algorithms and application of rule-based algorithms","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:47.976539Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:47fadaf03f9482912202bc091c6a4c2d83a721c7cfb0b01848de0613a0bf75e1","observation_id":"42be5450-42f3-4bd6-89f1-f82447885378","resolution":{"observed_at":"2026-08-04T11:38:47.976539Z","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":"2026-08-04T11:38:48.042793Z","title":"Performance of a machine learning algorithm using electronic health record data to identify and estimate survival in a longitudinal cohort of patients with lung cancer","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:48.042793Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:8275e8657112fc4be112c1d4110fe3d2365e1584b4a3ce354c3689917ad94d18","observation_id":"9e14ca2b-966c-4ff0-95b1-bbf42cc3e0c3","resolution":{"observed_at":"2026-08-04T11:38:48.042793Z","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":"2026-08-04T11:38:48.144408Z","title":"Using a data quality framework to clean data extracted from the electronic health record: A case study","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:48.144408Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:32d31955f67fb0e9b3ebb7fff8cb3845a81699ca15716b1d60eb8b971aa5fb15","observation_id":"f5f23f4f-6d27-4afe-b871-6437c63fea6f","resolution":{"observed_at":"2026-08-04T11:38:48.144408Z","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":"2026-08-04T11:38:48.236419Z","title":"A clustering approach for detecting implausible observation values in electronic health records data","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:48.236419Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:ea57c6882d0e58c543f30d3af1f7b64d8eda0ec9d9497da6394ea5abefae64ba","observation_id":"ada66214-e2b0-4210-abcc-87e08aae1b01","resolution":{"observed_at":"2026-08-04T11:38:48.236419Z","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":"2026-08-04T11:38:48.309020Z","title":"An automated data cleaning method for Electronic Health Records by incorporating clinical knowledge","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:48.309020Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:0b1b4f4d0d9d1d26abeb72583ed20913126d5a07adc81710a399607f45690d5f","observation_id":"e19d94e3-5f2c-48e8-94f4-1a8f68a4c026","resolution":{"observed_at":"2026-08-04T11:38:48.309020Z","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":"2026-08-04T11:38:48.363287Z","title":"Labeling of medicines and patient safety: Evaluating methods of reducing drug name confusion","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:48.363287Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:e6587214a1f0d1b99f96f504f002e656af2e8ce719700d0fe3f70176656a85ff","observation_id":"70575e16-9564-404e-a759-c218d8212fc3","resolution":{"observed_at":"2026-08-04T11:38:48.363287Z","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":"2026-08-04T11:38:48.410730Z","title":"Automated misspelling detection and correction in clinical free-text records","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:48.410730Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:150fe28e6fa78cf53a08ec6d2ec7508cfeb1a493a9f599e27e8a8a62d3b64110","observation_id":"e82efc89-897f-4a61-ba2e-dce9a65a9779","resolution":{"observed_at":"2026-08-04T11:38:48.410730Z","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":"2026-08-04T11:38:48.528678Z","title":"MLM-based typographical error correction of unstructured medical texts for named entity recognition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:48.528678Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:f57694d7b74439de877491acaf0b4c0911c1a3d41cb3334f88b6a7ddacb0177a","observation_id":"0e3d3afe-4a92-40ea-bd84-6bac47d57405","resolution":{"observed_at":"2026-08-04T11:38:48.528678Z","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":"2026-08-04T11:38:48.594738Z","title":"Automated identifica- tion of implausible values in growth data from pediatric electronic health records","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:48.594738Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:dea1b0c4bc0bba7ca363abc35754c18ed200068d52ebdfbc6b2dafc86f0111d3","observation_id":"e4624965-be98-4fa7-a727-6a51961b787b","resolution":{"observed_at":"2026-08-04T11:38:48.594738Z","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":"2026-08-04T11:38:48.682391Z","title":"Cleaning of anthropometric data from PCORnet electronic health records using automated algorithms","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:48.682391Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:c02ab8ec71846986d6629a3f300c60005ec6155bf09c733b2cef9a4a94ea2832","observation_id":"4d4e3991-13e3-4990-81d9-9290b0e64b8d","resolution":{"observed_at":"2026-08-04T11:38:48.682391Z","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":"2026-08-04T11:38:48.744196Z","title":"Identifying erroneous height and weight values from adult electronic health records in the All of Us research program","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:48.744196Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:3400a33bfcbedc4c8ebeb36bf75773adabe57c9c200ab7158b56d61ce4f2f5a8","observation_id":"4739e91a-e957-42b8-9b84-2b56bc5e6424","resolution":{"observed_at":"2026-08-04T11:38:48.744196Z","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":"2026-08-04T11:38:48.796985Z","title":"Strategies for handling missing data in electronic health record derived data","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:48.796985Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:78ccb8b11c1d240f30a82aaee74cb35432f354722ec774201800fa9d2a3007b0","observation_id":"1814cb7f-43bd-4732-aea6-fb12da2c379c","resolution":{"observed_at":"2026-08-04T11:38:48.796985Z","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":"2026-08-04T11:38:48.894741Z","title":"DensityTransfer: A data driven approach for imputing electronic health records","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:48.894741Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:e955fdf32b13663168601028031890dc5330e5f70b8f6570d7a4f8a66779017b","observation_id":"d71ae843-2f80-40e6-b728-f12bf0853299","resolution":{"observed_at":"2026-08-04T11:38:48.894741Z","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":"2026-08-04T11:38:48.950113Z","title":"Opportunities and challenges in developing risk prediction models with electronic health records data: A systematic review","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:48.950113Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:e277234e68087e1369181c3f84765953f513defb1d4289f01b9f8e4566259ed1","observation_id":"674dafe1-7614-40f5-be46-2ace612be7fa","resolution":{"observed_at":"2026-08-04T11:38:48.950113Z","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":"2026-08-04T11:38:49.015151Z","title":"Characterizing and managing missing structured data in electronic health records: Data analysis","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:49.015151Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:46937aab4fcb38ce537cad70b17ee9365da0ad4fccd796756e03b8bb852dbdf3","observation_id":"9850cad6-375b-4a34-9410-a4b9ec56f061","resolution":{"observed_at":"2026-08-04T11:38:49.015151Z","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":"2026-08-04T11:38:49.066504Z","title":"A deep learning–based unsupervised method to impute missing values in patient records for improved management of cardiovascular patients","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:49.066504Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:c1b37973ea7dd9e15f393620e2616d6bef86c5226f95bac3ffcdab8eb6b05da8","observation_id":"c7b84723-e9ff-47c9-bf54-a94856c1d96a","resolution":{"observed_at":"2026-08-04T11:38:49.066504Z","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":"2026-08-04T11:38:49.112179Z","title":"A novel missing data imputation approach based on clinical conditional generative adversarial networks applied to EHR datasets","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:49.112179Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:fb069bb32e23c469e549d208ee733d0d043d511f25f6611948b70a60f6a2cf22","observation_id":"c10c587a-a36f-4491-b7a5-fb9ec4e13ef3","resolution":{"observed_at":"2026-08-04T11:38:49.112179Z","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":"2026-08-04T11:38:49.161204Z","title":"Imputation of missing data in electronic health records based on patients’ similarities","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:49.161204Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:9f56fcbfe58bab3a570e2a6b30e1b28280181e47825c77ddc18fac8cd86ae60a","observation_id":"0e00f0a7-8d8e-48f6-96d7-ed7332789c1a","resolution":{"observed_at":"2026-08-04T11:38:49.161204Z","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":"2026-08-04T11:38:49.208836Z","title":"Don’t do imputation: Dealing with informative missing values in EHR data analysis","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:49.208836Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:886fdd52aea3e9c4c575d380b9a4ac605558c80411f8fad73c9b0b60df9b471c","observation_id":"4620a96e-f38b-498f-96e9-69b19dce23de","resolution":{"observed_at":"2026-08-04T11:38:49.208836Z","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":"2026-08-04T11:38:49.265795Z","title":"Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:49.265795Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:df4f9befd6a279b1e140ccf1b82587e53c7d9174749310bb757772dd8734da6f","observation_id":"8d44da6b-0ecf-42cd-94a0-e785fb677095","resolution":{"observed_at":"2026-08-04T11:38:49.265795Z","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":"2026-08-04T11:38:49.342353Z","title":"ellmer: Chat with Large Language Models; 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:49.342353Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:4cd0065c3f9664de9b837269a46de73bc946278b4d5dee3e7415bd270d1154d8","observation_id":"87acf42d-be63-4e31-9e9d-792f2b5bb69a","resolution":{"observed_at":"2026-08-04T11:38:49.342353Z","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":"2026-08-04T11:38:49.395393Z","title":"Gemini: A Family of Highly Capable Multimodal Models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:49.395393Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:3faf981720198454484a0308cd51ad969266c9671ddae0b47788f74a38a422af","observation_id":"7b4086b0-1c9f-4804-9f4e-159e27f30653","resolution":{"observed_at":"2026-08-04T11:38:49.395393Z","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":"2026-08-04T11:38:49.514032Z","title":"An evaluation of GPT models for phenotype concept recognition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:49.514032Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:5a31eb92c3fad8043b8782aae75d636d06bece1cdc5b404ddaef93fc85aeb0d0","observation_id":"1ef82735-3798-41aa-a1c8-c0f7da9950c6","resolution":{"observed_at":"2026-08-04T11:38:49.514032Z","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":"2026-08-04T11:38:49.642978Z","title":"Retrieving evidence from EHRs with LLMs: Possibilities and challenges","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:49.642978Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:88ddc059e431e2ee0d919bc1562e300e6a6ba0cdab5329af578ac9771bfaca59","observation_id":"89fc57f2-9e39-4120-9c19-7341197bd80d","resolution":{"observed_at":"2026-08-04T11:38:49.642978Z","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":"2026-08-04T11:38:49.797680Z","title":"Large language models for data extraction from unstructured and semi-structured electronic health records: a multiple model performance evaluation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:49.797680Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:986172b76ce238cdbf2b767d24851e435fccd3d283e972c1361390fabc9f68e6","observation_id":"4473f930-64b5-40b4-b5b3-e57a336549ea","resolution":{"observed_at":"2026-08-04T11:38:49.797680Z","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":"2026-08-04T11:38:49.996868Z","title":"Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:49.996868Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:971a42b8d7bd273baaa95ec909a13afeeea1c27926b5756bddd56c8390998dfe","observation_id":"37a81ff4-00f9-4404-8738-666d9f8d3a42","resolution":{"observed_at":"2026-08-04T11:38:49.996868Z","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":"2026-08-04T11:38:50.103954Z","title":"Increasing patient portal usage: Preliminary outcomes from the MyChart Genius Project","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:50.103954Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:a39f323b8f9aa4f1e70bb2d12d97bda204ddfd0bf027df6fc0a0b8e717d84b8b","observation_id":"cb301680-f336-4560-84ca-72d1e2d98b2d","resolution":{"observed_at":"2026-08-04T11:38:50.103954Z","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":"2026-08-04T11:38:50.269673Z","title":"From smartphone to EHR: A case report on integrating patient-generated health data","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:50.269673Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:521b20b9da38bb95bac659a0249b84a54d28730bebe71b8f0206b7d2b5a1a340","observation_id":"dbe7bc66-0652-4b83-b210-580f822b2565","resolution":{"observed_at":"2026-08-04T11:38:50.269673Z","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":"2026-08-04T11:38:50.472292Z","title":"GPT-4 Technical Report","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:50.472292Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:540b2293a7cb429326f38b867111546c67e62d43bbce18094a1a480d4faaf3ca","observation_id":"972c9bde-5767-4ee9-9aa8-d234c9cfbdd2","resolution":{"observed_at":"2026-08-04T11:38:50.472292Z","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":"2026-08-04T11:38:50.555399Z","title":"Introducing the next generation of Claude; 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:50.555399Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:5ad752f07802d90285f8e0d45e131b5671150a5564e2b5ff602f0f09a0340e09","observation_id":"c229581b-c075-4c53-8528-3f84e7911240","resolution":{"observed_at":"2026-08-04T11:38:50.555399Z","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":"2026-08-04T11:38:50.649647Z","title":"Introducing Meta Llama 3: The most capable openly available LLM to date; 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:50.649647Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:022284150089f2f79df816fb4416e85a527461b37e68e1e3000ea273997d8c45","observation_id":"9e160b8f-b44e-4f88-ac5b-6c16fea8b464","resolution":{"observed_at":"2026-08-04T11:38:50.649647Z","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":"2026-08-04T11:38:50.743454Z","title":"Copilot; 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:50.743454Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:091ad21b6e23e9dcbebac2f8037ba64c44ff42dc4d0d743f64943e41af9b8c13","observation_id":"e2df44e8-b780-464c-805d-1c9f123e9e2a","resolution":{"observed_at":"2026-08-04T11:38:50.743454Z","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":"2026-08-04T11:38:50.822817Z","title":"Frequency and types of patient-reported errors in electronic health record ambulatory care notes","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:50.822817Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:945d157c0daec25f610784e1a0522769c88bcaa6c4ee73cb989d01faed02dd14","observation_id":"f00bd714-f8aa-4d68-8eec-b45e8d72a5ec","resolution":{"observed_at":"2026-08-04T11:38:50.822817Z","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":"2026-08-04T11:38:51.047509Z","title":"Adolescents identifying errors and omissions in their electronic health records: A national survey","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:51.047509Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:fd3b84d8624889f08603e4b4ccc42634e50c1e584a6de3f360b4aff21f2b6595","observation_id":"2015a210-ce93-4ed3-9899-6953f6f02631","resolution":{"observed_at":"2026-08-04T11:38:51.047509Z","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":"2026-08-04T11:38:51.084378Z","title":"Learning about missing data mechanisms in electronic health records-based research: A survey-based approach","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:51.084378Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:089eb03c018328a10709d8cae577b783a519aab929a9025eddb4b8d9a8100b36","observation_id":"f69a8972-c53e-4cb6-821b-b06311456fde","resolution":{"observed_at":"2026-08-04T11:38:51.084378Z","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":"2026-08-04T11:38:51.225674Z","title":"Automated ICD coding via unsupervised knowledge integration (UNITE)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:51.225674Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:c172e1d33b59c86f456b46fb5a3c1c0bcf1784731347d34f9b3830aaa58fe830","observation_id":"32cb1233-dbcb-4a5f-89bb-bf81a8524ba6","resolution":{"observed_at":"2026-08-04T11:38:51.225674Z","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":"2026-08-04T11:38:51.299205Z","title":"Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-04T11:38:51.299205Z"},"links":{"citing_paper":"/paper/2510.03844"},"observation_digest":"sha256:bbee5a383adc07f1df5dc14960242c59bc10fc28e3437f51032f3e8088b00dba","observation_id":"8b1f4578-ce5c-4e7e-9055-ff2d4ff683fd","resolution":{"observed_at":"2026-08-04T11:38:51.299205Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2510.03844","last_updated":"2025-10-04T15:45:22Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T11:38:43.238008Z","submitted_at":"2025-10-04T15:45:22Z","title":"On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records"},"reference_resolution":{"displayed":83,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":83,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":83},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 0 inbound Pith citation observations for arXiv:2510.03844."}