{"as_of":"2026-08-13T05:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7ae128b1c6b826919d91cf6c704f1abc684d111d69ca6b0bbb733e2e35bea3ab","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T05:01:08.274158Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2606.26879/citation-record","integrity":"/paper/2606.26879/integrity","json":"/paper/2606.26879/citation-record.json","paper":"/paper/2606.26879"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T05:01:08.274158Z","title":"Robust de-anonymization of large sparse datasets","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:8605ed5fa40c8fb1e98b1ef714949f0afbb93de02e199598acf697da3d0d174a","observation_id":"8600aeba-7883-4977-af88-10abb8205a7c","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Synthetic data generation: State of the art in health care domain.Computer Science Review, 48:100546, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:68d2fe1da7a9f33344649c9d0cc8f7caf85a8ea755d788317676d8ad23b41780","observation_id":"5b41cae1-0685-41ea-90eb-bfc1abe54998","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Pezoulas, Dimitrios I","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:89a58980ffa8c4c5f7b9653368542364beb82c596a7f4a1157420a3944096d2a","observation_id":"28b2ad2f-be7b-453d-8fcc-02682e0caab2","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.06523","last_updated":"2021-04-13T21:44:29Z","snapshot_observed_at":"2026-07-06T10:59:23.123760Z","submitted_at":"2021-04-13T21:44:29Z","title":"A Review of Anonymization for Healthcare Data","version":1},"cited_work":{"arxiv_id":"2104.06523","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2104.06523","snapshot_observed_at":"2026-07-04T13:39:50.528281Z","title":"Olatunji, Jens Rauch, Matthias Katzensteiner, and Megha Khosla","venue":null,"work_id":"2135430e-eeb5-4778-963e-f6898028abe6","year":2021},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"cited_paper":"/paper/2104.06523","citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:1948254fc12f96c7c0444c4f6ffce01ec0fdc017fd53ee07b8fc3680dea1a84e","observation_id":"ee8922e7-90fe-40b8-a220-9d21f38a80db","resolution":{"observed_at":"2026-06-29T18:43:51.476206Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T05:01:08.274158Z","title":"Artificial data pilot, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:151f863d3fc24e66ac4fe898d7d79140fdb62a174f81f6f64706d33e6594d905","observation_id":"d2e1e7e7-fa7f-454e-8f8e-9d0e5674548a","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Synthea: Synthetic patient population simulator, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:397b11cc55a0e873f40439f9b8f40c2e8c5218c97dd3be07b54af3fd78c62514","observation_id":"3e19c98f-a2e4-4cfb-ba4a-24bbb9fca4b5","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Agent hospital: A simulacrum of hospital with evolvable medical agents, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:218db6b22a3adcaf8517956240f760a97ec4de682d95b0bf4d515b289f4f4b9f","observation_id":"4af4e84b-d273-4ea7-94ad-3f49de5c00ab","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Sdv (synthetic data vault) developer documentation, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:c7c7cf6fc27a67a59c9b9224b1f2375dd2a213c4062ccf2b36f2393f77dc77be","observation_id":"458e32a2-8540-41da-ad7d-9f99ac585dc8","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Swpc synthea: Uk adaptation of the synthea synthetic patient generator, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:d0fd62fd9b3493a59394efe5e082ac076b50a83425643eddef9c10466fc6cf69","observation_id":"b22b8643-2e83-4cea-8ba9-34f463f5eb2d","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Springer Nature Switzerland, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:90e2c365ee9238f734703c8b4e74958fee1cdf1f1657e96579ff684f90a8934a","observation_id":"33814419-b3e1-4981-bd5b-406ec7cce1af","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"A comprehensive taxonomy of hallucinations in large language models, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:decb503fd91594cc6e15ef3887cb0acd319d796013d3d778ea9922b1db204021","observation_id":"a9b2b5e4-1e87-4c25-bb0b-7b366bca0558","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Medical hallucination in foundation models and their impact on healthcare.medRxiv, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:659027acf922a32443743a8365b2fe30beca5bb35bacd16f869ed1cb87321969","observation_id":"4896931e-f433-4d93-84a7-992e287757bf","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:ac1fb6a02cc9c13f99f968d53ffd2562d254c33d2c10d14744b88262add9bba0","observation_id":"ffb86335-0eda-4e37-b4b2-0a213986cdfe","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Smith, Nima PourNejatian, Anthony B","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:aa1d08fd5226bfaa9a9a4bd2695ed968b5ad4d6fa4cfb8c0c688b2649e12fc44","observation_id":"75cb91e2-a657-4d68-888a-83dcb270d05f","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Chain-of-thought prompting elicits reasoning in large language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:ac1ca43ef6b426f14db7f87d46837b79ca89fdae19a76410949b1d303d9f3238","observation_id":"18883a39-7841-4e4e-99da-d8b01b48089b","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.13946","last_updated":"2025-01-19T11:19:25Z","snapshot_observed_at":"2026-08-12T11:10:36.955485Z","submitted_at":"2025-01-19T11:19:25Z","title":"Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks","version":1},"cited_work":{"arxiv_id":"2501.13946","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.13946","snapshot_observed_at":"2026-07-04T13:39:50.527667Z","title":"Hallucination mitigation using agentic ai natural language- based frameworks","venue":null,"work_id":"89d8ffd3-50e4-4562-bf4e-a0ae2a736c4e","year":2025},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"cited_paper":"/paper/2501.13946","citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:90733a6d7c15ced52396ca8792598b437df06d64484cdce4ee96dd5aa79e4701","observation_id":"4135ae20-9f26-402b-8744-f6dc98ab53a4","resolution":{"observed_at":"2026-06-29T18:43:51.478979Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T05:01:08.274158Z","title":"A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics.Information Fusion, 118:102963, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:f348ba47e708ab65e3e685fe96180157a7f809952bfc43a08bc73eb304fd5614","observation_id":"5ee7a3db-1b2e-4a49-aaa1-55665b9088df","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Benchmarking retrieval-augmented generation for medicine, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:aee7e76249da67ecf4fa9bd253c71209ee36a05fcdde29b0c21db4d9ea6b9a92","observation_id":"0db23f19-fcef-4cdf-9bda-62e6eaf1a766","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Reasoning-enhanced healthcare predictions with knowledge graph community retrieval, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:ed6a50261dd7ee56895350cf7c6fd5872861459a72be9953190de9d721f7db8b","observation_id":"d4f98a5d-9455-450c-8a79-946ab219f02b","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"A survey on llm-as-a-judge, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:304826bb0edbaec875be6ad4267dfa9958df49f77106531631dade9f2447e445","observation_id":"c2f2317f-8e86-45aa-b1d4-e1a025192e7d","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Justice or prejudice? quantifying biases in llm-as-a-judge, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:3d525d7f78152a3b97c6cf278ca462d23be86f9b3a6f9e6f104d96e525175487","observation_id":"6afc7cad-4aec-46d8-984a-465d10cb35d7","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"typo: A python package to simulate typographical errors, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:597d1dd08f467ae98b0b5215e0213c7d3040f9d676096c39743d34220debea1b","observation_id":"3a742162-d064-4412-9b09-c770ce7e2fe9","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","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":"2026-06-29T05:01:08.274158Z","title":"Evaluating gender bias in large language models in long-term care.BMC Medical Informatics and Decision Making, 25(1):274, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T05:01:08.274158Z"},"links":{"citing_paper":"/paper/2606.26879"},"observation_digest":"sha256:9c75ef9598266a477805d31ac15065e40caaeac66652f121e3e845bd37686bae","observation_id":"4fc47145-9835-4a2a-98f3-ba2235fabd29","resolution":{"observed_at":"2026-06-29T05:01:08.274158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.26879","last_updated":"2026-06-26T10:20:07Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-06T19:27:07.876221Z","submitted_at":"2026-06-25T11:08:40Z","title":"A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":23},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2606.26879."}