{"as_of":"2026-08-21T18:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:29e564efb7d8d52f434ef2ba586b7e2aaa6d0e9caca195574d7c69703d359f0f","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T21:38:08.055134Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"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/2412.04254/citation-record","integrity":"/paper/2412.04254/integrity","json":"/paper/2412.04254/citation-record.json","paper":"/paper/2412.04254"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.08607","last_updated":"2024-03-13T15:20:30Z","snapshot_observed_at":"2026-08-21T10:11:33.215334Z","submitted_at":"2024-03-13T15:20:30Z","title":"MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses using Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.08607","snapshot_observed_at":"2026-08-11T21:38:07.924350Z","title":"Medinsight: A multi-source context augmentation framework for generating patient-centric medical responses using large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.924350Z"},"links":{"cited_paper":"/paper/2403.08607","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:0983a30608b7774d5137c5f63ef6e3b2ac21b84c17739e2156371dc5ef72433b","observation_id":"54b84679-3b84-41f1-84c9-ed75fa8b1e03","resolution":{"observed_at":"2026-08-11T21:38:07.924350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.19473","last_updated":"2024-06-21T08:26:36Z","snapshot_observed_at":"2026-08-21T14:44:40.443192Z","submitted_at":"2024-02-29T18:59:01Z","title":"Retrieval-Augmented Generation for AI-Generated Content: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19473","snapshot_observed_at":"2026-08-11T21:38:07.928260Z","title":"Retrieval-augmented generation for ai-generated content: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.928260Z"},"links":{"cited_paper":"/paper/2402.19473","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:742b35c3accb4c0f1eca2f0092e2ac1eaae8a5c3d528dfa6f24b8da944543668","observation_id":"69ccd8db-ac2c-4645-ac18-6e783a4acd6c","resolution":{"observed_at":"2026-08-11T21:38:07.928260Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.438525Z","title":"Remembering what the doctor said: organization and adults’ memory for medical information,","venue":null,"work_id":"f4a006ea-fb66-4a4b-85bd-84022f379620","year":1996},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.931621Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:fd2c277d5d86dcb7e4b82aceea38037166e8744ec0bb022cac97192a6b8547b6","observation_id":"3e3c02f6-3bdc-4d2e-b294-ee62c0bcbbd8","resolution":{"observed_at":"2026-08-11T21:38:08.441972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.427932Z","title":"Patient information recall in a rheumatology clinic,","venue":null,"work_id":"6ce7b072-409f-4d1a-b946-440b898658ae","year":1979},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.934910Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:95f30ef7b4439628ad7ea491a87bb0be70a5a8abd37f98f7e3b4a791c017eed1","observation_id":"baf91ca3-1f4e-42e7-8fb3-75ee8b387ac1","resolution":{"observed_at":"2026-08-11T21:38:08.432195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.418457Z","title":"Burnout and doctors: prevalence, prevention and interven- tion,","venue":null,"work_id":"297bbee7-5477-46b5-a04a-a7f5c7c24074","year":2016},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.938055Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:3e11b8244f3169687074b1e53a621ec3e1facfab7d39c85a1f54a0e538e317a6","observation_id":"cfa30443-0aa4-4ef4-b2bb-d3dca1971414","resolution":{"observed_at":"2026-08-11T21:38:08.421856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.02549","last_updated":"2022-05-06T07:48:48Z","snapshot_observed_at":"2026-08-21T15:47:12.678837Z","submitted_at":"2022-05-05T10:18:06Z","title":"User-Driven Research of Medical Note Generation Software","version":2},"cited_work":{"arxiv_id":"2205.02549","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.02549","snapshot_observed_at":"2026-08-11T21:38:08.213121Z","title":"User-Driven Research of Medical Note Generation Software","venue":"cs.HC","work_id":"583c50a5-7f43-4b20-b3e0-d004854d7a57","year":2022},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.941765Z"},"links":{"cited_paper":"/paper/2205.02549","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:73700fc52dd0075e7fbfd2b7f376ae3b46f5af91ef43ca6619d42b67f82df899","observation_id":"3f4cc1e9-c28a-46be-b275-a05dbb95cb9e","resolution":{"observed_at":"2026-08-11T21:38:08.216538Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.05232","last_updated":"2024-11-19T12:42:45Z","snapshot_observed_at":"2026-07-06T16:45:07.733095Z","submitted_at":"2023-11-09T09:25:37Z","title":"A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.05232","snapshot_observed_at":"2026-08-11T21:38:07.945431Z","title":"A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.945431Z"},"links":{"cited_paper":"/paper/2311.05232","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:9a0889f6c217ed8b8be9e768b277b402d15e6fa2e38b6fe950b987f8d0d37fa1","observation_id":"69a98229-6be5-4548-9e80-83418ef96b3f","resolution":{"observed_at":"2026-08-11T21:38:07.945431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.409201Z","title":"An empirical study of clinical note generation from doctor-patient encounters,","venue":null,"work_id":"513d4fa8-d9ac-41a2-a859-bf6f394f8cf4","year":2023},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.948623Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:e3b06f6030c8df75c574e8d79cff5cba27409f875bd82dd5dafc909a54cee54c","observation_id":"9e260abb-1832-4294-817b-9c9c511e7655","resolution":{"observed_at":"2026-08-11T21:38:08.412790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.12174","last_updated":"2021-09-24T20:18:59Z","snapshot_observed_at":"2026-08-18T10:49:07.238513Z","submitted_at":"2021-09-24T20:18:59Z","title":"Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations","version":1},"cited_work":{"arxiv_id":"2109.12174","doi":null,"metadata_source":"pith","pith_arxiv_id":"2109.12174","snapshot_observed_at":"2026-08-11T21:38:08.193257Z","title":"Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations","venue":"cs.CL","work_id":"a3807115-b7de-4725-8126-a4e30913b9bb","year":2021},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.951624Z"},"links":{"cited_paper":"/paper/2109.12174","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:74d35642100c47719ccebf43b69938673666525cf9335e837af867e92860d05f","observation_id":"4db8822c-c235-4ffe-be6a-e9da253b230c","resolution":{"observed_at":"2026-08-11T21:38:08.196675Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.02220","last_updated":"2023-06-03T17:56:29Z","snapshot_observed_at":"2026-08-18T20:17:22.805269Z","submitted_at":"2023-05-03T15:58:28Z","title":"WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models","version":2},"cited_work":{"arxiv_id":"2305.02220","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.02220","snapshot_observed_at":"2026-08-11T21:38:08.180482Z","title":"WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models","venue":"cs.CL","work_id":"93ec74a1-d95c-40e1-aa20-c7d1498f372c","year":2023},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.954792Z"},"links":{"cited_paper":"/paper/2305.02220","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:2b0a41d0f4747feda233563e2b3d5122601a246750b3de97ed1a1d5a6a250ced","observation_id":"d4a5e1de-1bba-4ee8-8263-3b860c03ae9a","resolution":{"observed_at":"2026-08-11T21:38:08.184334Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:07.958023Z","title":"Language mod- els are few-shot learners,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.958023Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:50998a2704d0b10bbe5a55dba124575081ba449adf8ffd3288cbad53aa29e952","observation_id":"940151a8-c6bc-4682-887c-ea9bd336a2ec","resolution":{"observed_at":"2026-08-11T21:38:07.958023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.394161Z","title":"Towards an automated soap note: classi- fying utterances from medical conversations,","venue":null,"work_id":"39470f56-3b88-4405-9349-9b724e00f0f8","year":2020},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.960953Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:27dbcb7de84ef171f51a2c98b1559a0e44600a385fde7fb5830cff169a9db7c0","observation_id":"2fe7c28a-b22e-462f-9164-87e78ebccfa0","resolution":{"observed_at":"2026-08-11T21:38:08.397524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.383682Z","title":"Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties,","venue":null,"work_id":"e675bc16-332e-4bf3-9499-5cea558620b9","year":2016},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.963760Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:0e486abea5c30a7fea51d35c8a52133c6746f65f2f0305879cefa94bc9dd3819","observation_id":"36f2800e-e1ca-4439-92c6-c066aef35729","resolution":{"observed_at":"2026-08-11T21:38:08.387722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.374263Z","title":"The problem oriented record as a basic tool in medical education, patient care and clinical research","venue":null,"work_id":"9bb8d72c-81cc-4cd3-87b3-7020bad90f8a","year":1971},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.966637Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:1dcf5db4e9e4f62943320b582e429b999a47a057afb609c7da6191d2d9cb2f56","observation_id":"5eccfadf-d502-4aa8-96b4-767071d32bdc","resolution":{"observed_at":"2026-08-11T21:38:08.377673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:07.969508Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.969508Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:f0851a753d68685f6b7f5d2519df2cf6f63836ddbfc527a18874959a03a4fdb5","observation_id":"63961b7f-4ff3-49d0-9d09-08267af8b055","resolution":{"observed_at":"2026-08-11T21:38:07.969508Z","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":"2026-08-11T21:38:07.972396Z","title":"Recent advances in natural language processing via large pre-trained language models: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.972396Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:5a2eb5924566c08c1aa097761f1b1a3e029ac064fade728c1a7d202ab96c51d8","observation_id":"797c4f57-6c25-4043-8020-a619190b1852","resolution":{"observed_at":"2026-08-11T21:38:07.972396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07521","last_updated":"2023-12-16T12:47:19Z","snapshot_observed_at":"2026-08-18T10:45:30.869066Z","submitted_at":"2023-10-11T14:18:03Z","title":"Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.07521","snapshot_observed_at":"2026-08-11T21:38:07.975215Z","title":"Survey on factuality in large language models: Knowledge, retrieval and domain-specificity,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.975215Z"},"links":{"cited_paper":"/paper/2310.07521","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:5763701a61bfeeeb9ce3254af72e6d72819dbde81e86fbce2dde63b40b9611b5","observation_id":"ca536a4f-61a6-46cf-b964-9d2eb9e6c232","resolution":{"observed_at":"2026-08-11T21:38:07.975215Z","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":"2026-08-11T21:38:07.978488Z","title":"Overcoming catastrophic forgetting in neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.978488Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:16f8c52abc7fc74469ee8cb80cf168bbd820de6c683e2fcf9797075f8fb235eb","observation_id":"5d88be82-de31-4953-abc1-d87b19acdd53","resolution":{"observed_at":"2026-08-11T21:38:07.978488Z","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":"2026-08-11T21:38:07.981241Z","title":"Retrieval- augmented generation for knowledge-intensive nlp tasks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.981241Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:08306afdcde4048d546577a2c1ebc2ddbd6cf597173fc674eb488fefa6d4b949","observation_id":"4495a3eb-8b37-4725-b582-cca2a7b040e4","resolution":{"observed_at":"2026-08-11T21:38:07.981241Z","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":"2026-08-11T21:38:07.984310Z","title":"Parameter-efficient transfer learning for nlp,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.984310Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:07eb4d61deb710da454f9c397d309b2270a3b0929379f594b56f4277ba34ce0e","observation_id":"d989808d-d705-4970-b839-abbc6ff825bf","resolution":{"observed_at":"2026-08-11T21:38:07.984310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08691","last_updated":"2021-09-02T17:34:41Z","snapshot_observed_at":"2026-08-16T20:01:36.160048Z","submitted_at":"2021-04-18T03:19:26Z","title":"The Power of Scale for Parameter-Efficient Prompt Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08691","snapshot_observed_at":"2026-08-11T21:38:07.987220Z","title":"The power of scale for parameter-efficient prompt tuning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.987220Z"},"links":{"cited_paper":"/paper/2104.08691","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:3c4103b8ada04a83816b3865215eb6a4a968337bec0556773f83d9ab5babd655","observation_id":"72bec3bd-be37-4e0e-92c6-ef35477257ed","resolution":{"observed_at":"2026-08-11T21:38:07.987220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.07558","last_updated":"2023-04-19T04:28:24Z","snapshot_observed_at":"2026-08-18T10:49:26.769534Z","submitted_at":"2022-10-14T06:29:22Z","title":"DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.07558","snapshot_observed_at":"2026-08-11T21:38:07.990347Z","title":"Dylora: Parameter efficient tuning of pre-trained models using dynamic search- free low-rank adaptation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.990347Z"},"links":{"cited_paper":"/paper/2210.07558","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:d59bef3ecc5e92bf3b6d43cbe37bd1a3a2b7253df811c65e2f2981f64790ab3e","observation_id":"c57f8b6a-1c00-40c9-afee-22af9efc0d07","resolution":{"observed_at":"2026-08-11T21:38:07.990347Z","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":"2026-08-11T21:38:07.993464Z","title":"Qlora: Efficient finetuning of quantized llms,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.993464Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:6f7b27fc5515fa4879b78938572414404f5fba1e8d0e59d256f88a352eee4d44","observation_id":"594f9073-1d34-4b07-87c0-056e7b8db6d9","resolution":{"observed_at":"2026-08-11T21:38:07.993464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.329918Z","title":"Abstractive dialogue summarization with sentence-gated modeling optimized by dialogue acts,","venue":null,"work_id":"66bc32b7-1ca5-49b4-9b7b-6407b8aab9a2","year":2018},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.996324Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:99ebfe39a74dd97b37892f2281bbea71613b746594de51dd8f0afedbe6209f38","observation_id":"3b87e3a8-17d3-47a3-bf6c-923ac504d0c8","resolution":{"observed_at":"2026-08-11T21:38:08.333226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.320787Z","title":"Keep meeting summaries on topic: Abstractive multi-modal meeting summarization,","venue":null,"work_id":"55ed140f-1ee3-4bac-9c63-8508ee2f62f4","year":2019},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:07.999331Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:beec00db67f0807830d534efb5fb1ac43e7864db1fabfc8efe07b889960266af","observation_id":"25fdfdd9-1ea2-4250-a985-c7da0ce302be","resolution":{"observed_at":"2026-08-11T21:38:08.324066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1809.04698","last_updated":"2018-10-08T20:30:20Z","snapshot_observed_at":"2026-08-18T10:49:25.163408Z","submitted_at":"2018-09-12T22:41:47Z","title":"Learning to Summarize Radiology Findings","version":2},"cited_work":{"arxiv_id":"1809.04698","doi":null,"metadata_source":"pith","pith_arxiv_id":"1809.04698","snapshot_observed_at":"2026-08-11T21:38:08.144713Z","title":"Learning to Summarize Radiology Findings","venue":"cs.CL","work_id":"c91533f3-f17d-4656-a879-e6ed8d161196","year":2018},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.002210Z"},"links":{"cited_paper":"/paper/1809.04698","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:173d6ed4a98558e021b70251d9d23b098610140849a506d64d60a06b724ad938","observation_id":"fd3200dd-d097-4f27-87ec-357916ea2908","resolution":{"observed_at":"2026-08-11T21:38:08.148109Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.311618Z","title":"An automated medical scribe for documenting clinical encounters,","venue":null,"work_id":"a46277ee-b19b-43c1-a660-198e1e6b870f","year":2018},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.005263Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:9d326fc014c6cfe1557de4838935d58a37a171a50cab93de21cb5a4d2022196f","observation_id":"9801d650-b719-45e5-bcb9-1d3063e40edd","resolution":{"observed_at":"2026-08-11T21:38:08.314906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.302452Z","title":"Generating medical reports from patient-doctor conversations using sequence-to-sequence models,","venue":null,"work_id":"866cc3bb-eb1c-446f-a1c5-09496cf0a17b","year":2020},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.008626Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:264058c368bc5c286fd5ccae574f53c065b567270396a1a99cfd8ad08e2e2029","observation_id":"9e0d5a59-542d-4363-afbf-b507e15a9540","resolution":{"observed_at":"2026-08-11T21:38:08.305688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.01795","last_updated":"2021-06-02T14:48:09Z","snapshot_observed_at":"2026-08-18T10:49:24.657353Z","submitted_at":"2020-05-04T19:10:26Z","title":"Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques","version":3},"cited_work":{"arxiv_id":"2005.01795","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.01795","snapshot_observed_at":"2026-08-11T21:38:08.132200Z","title":"Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques","venue":"cs.CL","work_id":"ca0d2f5d-5a68-47f0-8916-4b84d9344fce","year":2020},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.011637Z"},"links":{"cited_paper":"/paper/2005.01795","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:7fe0ce04d40b904bfdbf53150613fe6f384d0113132055cacc2c1aa6d254de3e","observation_id":"723f0c2f-f288-42ef-8788-786c2374e60c","resolution":{"observed_at":"2026-08-11T21:38:08.135829Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.293343Z","title":"Generating more faithful and consistent soap notes using attribute-specific parameters,","venue":null,"work_id":"4fa97248-78a0-425c-8990-af008c600743","year":2023},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.014861Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:5900dbbf27b992024f458886d04881624d43cbfa0b93d9cab4b9df5dbabae51a","observation_id":"8997d9e4-daf1-466b-a332-87217c0dabeb","resolution":{"observed_at":"2026-08-11T21:38:08.296695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.017757Z","title":"Language models are unsupervised multitask learners,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.017757Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:5d1609ef708d045cc9dd7dd3d8799a18e417f16f1cec2f19a83451d756eb290f","observation_id":"cbe578e3-6769-4abc-9c94-fdfafdcf9eb6","resolution":{"observed_at":"2026-08-11T21:38:08.017757Z","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":"2026-08-11T21:38:08.020603Z","title":"Okapi at trec-3,","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.020603Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:6ca2b2ff47792492fc6f492f5d6e07ad7c65c3386ae6f8ca0271e1122dd79141","observation_id":"8faf5fd1-f64d-4b57-86bd-2d0fb2aa5e18","resolution":{"observed_at":"2026-08-11T21:38:08.020603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.04906","last_updated":"2020-09-30T21:27:13Z","snapshot_observed_at":"2026-07-06T09:11:26.109763Z","submitted_at":"2020-04-10T04:53:17Z","title":"Dense Passage Retrieval for Open-Domain Question Answering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.04906","snapshot_observed_at":"2026-08-11T21:38:08.023422Z","title":"Dense passage retrieval for open-domain question answering,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.023422Z"},"links":{"cited_paper":"/paper/2004.04906","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:faff8f9497cdbad0d8472f94843e95099f59a4618a754b43b04ccdf82ae4ff26","observation_id":"ab055f65-5001-4eed-b335-c1a7ad5c1f37","resolution":{"observed_at":"2026-08-11T21:38:08.023422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.273513Z","title":"Experimental approach toward training and analysing siamese deep neural network for sentence with no repeated expressions,","venue":null,"work_id":"e220c5c6-4d13-4a7a-8046-0ec52cfe90d2","year":2023},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.026724Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:bc33dd387cd54fac3ae696c6e9d1bba12be844e3e3610790ef3172a3a75b77d2","observation_id":"40e90e88-5c09-4252-a783-859785ae239d","resolution":{"observed_at":"2026-08-11T21:38:08.276960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.029932Z","title":"Reciprocal rank fusion outperforms condorcet and individual rank learning methods,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.029932Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:3daca7a05a8d2a93fef81e04447c182df725e634a54746c742fd313221c71696","observation_id":"e0b0b2d5-de78-463e-8123-5029dc60b3e3","resolution":{"observed_at":"2026-08-11T21:38:08.029932Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-20T11:47:17.477107Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-11T21:38:08.033704Z","title":"Lora: Low-rank adaptation of large language models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.033704Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:dff1687903c287006d4a915864515ee30d2674e9864b568c4d9d914970340949","observation_id":"a46a74d1-52ae-4427-be74-aa57316063f1","resolution":{"observed_at":"2026-08-11T21:38:08.033704Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.258171Z","title":"How to fine-tune: Focus on effective datasets,","venue":null,"work_id":"e7c1a426-7e13-4c97-bdba-e82271896724","year":2024},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.036823Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:3a73548a043444f46c8606cd3041cbd2529726f7dd080cd1aa8bb7e5aa8ca24e","observation_id":"b11870b6-c019-4613-8a3a-26b1acc224f1","resolution":{"observed_at":"2026-08-11T21:38:08.261613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.039684Z","title":"Stanford alpaca: An instruction-following llama model,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.039684Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:121c92a088fd3626a72f2b21eb9c8d68ea0c629d77bffc5916f8ecbf8f8a1002","observation_id":"0238773b-dd3e-4e10-b25c-0dc4b2522bb3","resolution":{"observed_at":"2026-08-11T21:38:08.039684Z","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":"2026-08-11T21:38:08.042608Z","title":"Rouge: A package for automatic evaluation of summaries,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.042608Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:af6db982aaf6fc103094bb037d3a8e65d8e9ade3db38eee25dec71c80da5f985","observation_id":"1efbe060-475a-42c7-9487-e3470c735e37","resolution":{"observed_at":"2026-08-11T21:38:08.042608Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.09675","last_updated":"2020-02-24T18:59:28Z","snapshot_observed_at":"2026-07-29T15:42:51.774083Z","submitted_at":"2019-04-21T23:08:53Z","title":"BERTScore: Evaluating Text Generation with BERT","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.09675","snapshot_observed_at":"2026-08-11T21:38:08.045670Z","title":"Bertscore: Evaluating text generation with bert,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.045670Z"},"links":{"cited_paper":"/paper/1904.09675","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:dd7c4fd2c2a551e0e59e913d3f944373eac27e1766f58d406aa2857b65aad5e6","observation_id":"94783fe2-5264-4d36-8a59-650bc0d78109","resolution":{"observed_at":"2026-08-11T21:38:08.045670Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.10216","last_updated":"2022-04-21T15:52:14Z","snapshot_observed_at":"2026-08-18T10:49:08.266877Z","submitted_at":"2022-04-21T15:52:14Z","title":"Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics","version":1},"cited_work":{"arxiv_id":"2204.10216","doi":null,"metadata_source":"pith","pith_arxiv_id":"2204.10216","snapshot_observed_at":"2026-08-11T21:38:08.095587Z","title":"Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics","venue":"cs.CL","work_id":"9f535bc2-8a47-4ae6-b01f-d1b94c4ef5d2","year":2022},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.048942Z"},"links":{"cited_paper":"/paper/2204.10216","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:1ff033d4c7488b8c4849ca8896f742478dfba794d0d47396f1a0ad89cca8383e","observation_id":"9701622c-1db4-48bd-b303-159b4a920d42","resolution":{"observed_at":"2026-08-11T21:38:08.099209Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.09455","last_updated":"2022-11-17T10:54:28Z","snapshot_observed_at":"2026-08-18T22:17:18.873548Z","submitted_at":"2022-11-17T10:54:28Z","title":"Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation","version":1},"cited_work":{"arxiv_id":"2211.09455","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.09455","snapshot_observed_at":"2026-08-11T21:38:08.080746Z","title":"Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation","venue":"cs.CL","work_id":"22ae78ab-1141-4774-87ca-213d7570aeb5","year":2022},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.052081Z"},"links":{"cited_paper":"/paper/2211.09455","citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:5fd526317d9b6ee363a811d5fbb7d09d093f3fe9d7b15d656e8fa0ac3577ef24","observation_id":"006c99f0-8978-431b-a5bf-aaefcc99bbca","resolution":{"observed_at":"2026-08-11T21:38:08.086000Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:38:08.055134Z","title":"Interrater reliability: the kappa statistic,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T21:38:08.055134Z"},"links":{"citing_paper":"/paper/2412.04254"},"observation_digest":"sha256:f625e3ea16765c96c6d32d22959b4f7bfc2b506d46234d3d876aaf3c3e24ba8e","observation_id":"4956c692-6b78-4788-8ede-334f9b41c8c8","resolution":{"observed_at":"2026-08-11T21:38:08.055134Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.04254","last_updated":"2024-12-05T15:34:02Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-20T09:40:53.961969Z","submitted_at":"2024-12-05T15:34:02Z","title":"CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":7,"verified_fuzzy":14},"total_outbound_references":43},"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 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2412.04254."}