{"as_of":"2026-08-07T23:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8f39552933575bc4d91c66e5b7fbc3a8117cc2cea756a4a30bc509119a2a1a9f","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-22T00:05:21.750651Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-21T01:31:07.031424Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":1,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"cited_work":{"arxiv_id":"2507.01053","doi":"10.48550/arxiv.2507.01053","metadata_source":"pith","pith_arxiv_id":"2507.01053","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","venue":"cs.IR","work_id":"7f5a9088-5186-447e-aee8-70b3f67e0376","year":2025},"citing_paper":{"arxiv_id":"2604.12258","last_updated":"2026-04-21T05:52:08Z","snapshot_observed_at":"2026-08-02T14:08:00.602083Z","submitted_at":"2026-04-14T04:22:44Z","title":"Coding-Free and Privacy-Preserving Agentic Framework for Data-Driven Clinical Research","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T16:09:57.634528Z"},"links":{"cited_paper":"/paper/2507.01053","citing_paper":"/paper/2604.12258"},"observation_digest":"sha256:311a815d6d35ae5609d8edf823b12b92de62c3062f29eb5f98f4e1e561fc6cf4","observation_id":"a0afb4f6-3b3f-4ed3-9f41-0f2186b82b88","resolution":{"observed_at":"2026-05-11T09:16:00.783687Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"cited_work":{"arxiv_id":"2507.01053","doi":"10.48550/arxiv.2507.01053","metadata_source":"pith","pith_arxiv_id":"2507.01053","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","venue":"cs.IR","work_id":"7f5a9088-5186-447e-aee8-70b3f67e0376","year":2025},"citing_paper":{"arxiv_id":"2605.18768","last_updated":"2026-04-13T10:07:03Z","snapshot_observed_at":"2026-08-01T02:49:14.468592Z","submitted_at":"2026-04-13T10:07:03Z","title":"ClinQueryAgent: A Conversational Agent for Population Health Management","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-21T01:31:07.031424Z"},"links":{"cited_paper":"/paper/2507.01053","citing_paper":"/paper/2605.18768"},"observation_digest":"sha256:d7922813a5f96441aa66c665cc1154406c2c429d089c348290fe46f7da2651f1","observation_id":"965b92a8-13c8-4d30-9b9b-ca62bd81c8e9","resolution":{"observed_at":"2026-05-21T01:33:56.146203Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.01053/citation-record","integrity":"/paper/2507.01053/integrity","json":"/paper/2507.01053/citation-record.json","paper":"/paper/2507.01053"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Electronic health records: then, now, and in the future.Yearbook of medical informatics, 25(S 01):S48–S61","venue":null,"work_id":"fa655986-1816-4ae4-b119-eed3c5b1196b","year":2016},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:1d13044e35ea69539dd3812a6ab5f04816137496747e7569e40f41f3c6e38547","observation_id":"da94be43-2746-4ad1-aa0b-76590b07dbc1","resolution":{"observed_at":"2026-05-22T00:05:47.815789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4d4ea52d-46b8-4323-b9b0-d1294a5d4b67","year":2023},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:5ee4dfbc344d9a138d2d5dc73ff4222e2dbb67b5e1dc4bc41acb8ace0f308c4d","observation_id":"4378be2c-284e-4e83-9ecc-da86fe6cfcea","resolution":{"observed_at":"2026-05-22T00:05:47.853781Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Generative ai tracker","venue":null,"work_id":"3baf7a3b-ad62-4535-9346-48703c94364a","year":2025},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:baba032e3e407b931d9b9ecba781caedaa01cd3c90fe2e8a79711b88835f12cf","observation_id":"b357b4b9-9beb-424a-b071-d2536dabcc9e","resolution":{"observed_at":"2026-05-22T00:05:47.847104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Health system-scale language models are all-purpose prediction engines.Nature, 619(7969):357–362","venue":null,"work_id":"b0b803d0-776a-43a2-ad8f-9619e367988b","year":2023},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:5a0737672054d6099f70fc6c55d5e2223bc9649ac3e9b3222380dc1b7f6d6bad","observation_id":"605f835d-4323-4497-9df6-feb07ea93534","resolution":{"observed_at":"2026-05-22T00:05:47.837952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1056/aics2300191","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Abel, Mary Tolikas, and Jason M","venue":"NEJM AI","work_id":"86c0730d-7848-45b5-b14e-41d4292bf500","year":2024},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:8ce4fca20ebc4a4758382c2b0187c425e1851ac1db80b8090179da6f08290f67","observation_id":"05f52292-82c4-429b-af5b-401ca109782b","resolution":{"observed_at":"2026-05-22T00:05:47.422735Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Model context protocol (mcp)","venue":null,"work_id":"4010f309-45f8-47fd-825c-5ccdff5c6a03","year":2024},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:f80a4272b4bd4415775892d3c806070e409759b0b5bd34a2324f1a98afa04a81","observation_id":"12c1b659-c012-4063-9964-869280aa450a","resolution":{"observed_at":"2026-05-22T00:05:47.840219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Celi, and Roger Mark","venue":null,"work_id":"16f2b609-46d5-444a-ab21-a3b6c5252f50","year":2024},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:af9c1c1d2592a67c581045ff1739ea4116471a033fc947d8ac452bde97309ca4","observation_id":"2773e2bb-4fd5-4f1c-91e8-046c3ee99a15","resolution":{"observed_at":"2026-05-22T00:05:47.827091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals.Circulation, 101(23):e215–e220","venue":null,"work_id":"c1384173-6d30-49aa-9fbd-131f7eb88eba","year":2000},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:5b08b1a153677ef44d4e33d1999c856b5ad776551f64ce459728a583041363cf","observation_id":"6ef8c678-c0e0-452e-bc8a-181af9e7bd6a","resolution":{"observed_at":"2026-05-22T00:05:47.820718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"EHRSQL 2024 Dataset – MIMIC-IV Test Set","venue":null,"work_id":"494f30a4-550d-437a-9e46-421415c31d85","year":2024},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:de899581c9ef1c20be280ff02f7c2460e87e0fe45687bd4cd12bd9aa47c052ed","observation_id":"49ebe91e-787e-47df-98ca-ea5163329e4d","resolution":{"observed_at":"2026-05-22T00:05:47.810725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.13026/dp1f-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mimic-iv clinical database demo (version 2.2)","venue":null,"work_id":"1e2f7983-0f05-47e0-8989-d21796b155fc","year":2023},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:107057eeec53077b86bc7ec64ac11acfeceaa3201e94dabbad5fbc42c4d784ee","observation_id":"56b9239c-1bc1-4acb-8761-2990baa33488","resolution":{"observed_at":"2026-05-22T00:05:47.432853Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Reisner, Gari Clifford, Li-wei Lehman, George Moody, Thomas Heldt, Tin H","venue":null,"work_id":"270adf76-1e06-402b-8d0a-f479d59e1a0a","year":2011},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:2a79d26bbd04ce6d559fd687b45bc9bc91977d21651c3309c2a9398c2b9827c7","observation_id":"bfbefece-c3a1-4398-8bf1-f2bccee39de7","resolution":{"observed_at":"2026-05-22T00:05:47.831187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Mimic-iv (version 2.2)","venue":null,"work_id":"dbfe92b4-c2eb-43c3-9f7d-c416e594954c","year":2022},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:e7f47ddcc0635f1f20ac9ea6057eec6cb34fe8fc1b4d26e262a24c7f3f6b4d65","observation_id":"6d36b574-051a-431e-9a9a-10a0f48e4043","resolution":{"observed_at":"2026-05-22T00:05:47.833370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Sqlucid: Grounding natural language database queries with interactive explanations","venue":null,"work_id":"11dd0426-a328-411d-87ba-8369cd076721","year":2024},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:b0f6f63e4b2a4c40083b785769d5e4c0c7e6018da45a5c85e5952d09af5c0868","observation_id":"70d9e5df-22a6-459c-af28-c236e6f14108","resolution":{"observed_at":"2026-05-22T00:05:47.835519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Hl7 fhir standard, Accessed 2025","venue":null,"work_id":"b12aeb0f-0310-4aa8-a945-a983b4c5b1c1","year":2025},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:1d29ea9555e0ec7bae6c8b81097d77d66f5d25f1c1080f4b4227e8e8d361097a","observation_id":"205ca3df-f037-4a3f-9e52-3ba12c7cd0c1","resolution":{"observed_at":"2026-05-22T00:05:47.829168Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Mimic-iv on fhir: converting a decade of in-patient data into an exchangeable, interoperable format.Journal of the American Medical Informatics Association, 30(4):718–725","venue":null,"work_id":"8726d143-e380-4d83-8dd9-8dc30d4ecf0c","year":2023},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:34af4360a6bec3d98183224b8fa615bb09b94499f4063b400115ce6d659cebe1","observation_id":"e33d8dad-83b9-4332-b423-51963897a330","resolution":{"observed_at":"2026-05-22T00:05:47.798710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Standardized data: The omop common data model, Accessed 2025","venue":null,"work_id":"f7e718bd-f042-4967-a585-ff3f57400b65","year":2025},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:9d7180aa6e17d770eb79b85461af36ed1428796c1465716a4c604cb98bf7739a","observation_id":"58aab709-3f23-4e7b-94af-15931f32c0ba","resolution":{"observed_at":"2026-05-22T00:05:47.822932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Text-to-sql generation for question answering on electronic medical records","venue":null,"work_id":"ba48c8a4-c1ac-490c-b214-df8bc56a4822","year":2020},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:3690dde086a0b3d220fe9b5e759586e5526843d7752a15126fe9d25f1088d836","observation_id":"aaa54dfa-93b4-4ff2-99c7-7a636e182e77","resolution":{"observed_at":"2026-05-22T00:05:47.796211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Ehrsql: A practical text-to-sql benchmark for electronic health records.Advances in Neural Information Processing Systems, 35:15589–15601","venue":null,"work_id":"94136504-4062-4021-81fc-46064fc2a563","year":2022},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:b84c25c020faced94c9acad31f9a3cd3e74ea72f4ecb8ef46e31a14750b50058","observation_id":"cae9a8e7-8605-4e2d-b938-fb1cd79986e8","resolution":{"observed_at":"2026-05-22T00:05:47.801350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls.Advances in Neural Information Processing Systems, 36","venue":null,"work_id":"de6e8425-5bb5-4f34-8fac-eda45390cc7b","year":2024},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:5cb0389ddee8b64f3aec74beb49dad7c6312e37f73fbb37355e9b5bdc0a6c113","observation_id":"30439d13-48cb-406f-8a00-470071578e5e","resolution":{"observed_at":"2026-05-22T00:05:47.818014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Spider: A large-scale human-labeled dataset for complex and cross-domain text-to-sql tasks","venue":null,"work_id":"50da0b43-dbbc-4403-8742-37dedaa39eba","year":2018},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:c1424dec30d793feff7191d96457b2b9d65dbafe4c8db142c58f1d81c8760276","observation_id":"4427af31-23d0-4dd4-85a2-463651436fb8","resolution":{"observed_at":"2026-05-22T00:05:47.849501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1709.00103","last_updated":"2017-11-09T23:06:14Z","snapshot_observed_at":"2026-08-03T05:45:09.122112Z","submitted_at":"2017-08-31T23:12:15Z","title":"Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning","version":7},"cited_work":{"arxiv_id":"1709.00103","doi":"10.48550/arxiv.1709.00103","metadata_source":"pith","pith_arxiv_id":"1709.00103","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning","venue":"cs.CL","work_id":"75c8384b-6360-4be2-aaaf-07f3fd75aa06","year":2017},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"cited_paper":"/paper/1709.00103","citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:8468082226887295d4cb650e0df41094b0e2ba0ce7fe3192272f5e3f27b5b1b0","observation_id":"df7f622a-2c6a-4492-a4e7-342e9466f1c4","resolution":{"observed_at":"2026-05-22T00:05:47.457876Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2505.20321","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Biomedsql: Text-to-sql for scientific reasoning on biomedical knowledge bases","venue":null,"work_id":"a0a2df55-41fa-4987-be99-5caae59a3fc0","year":2025},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:3cbc5e92a1476731cbfd1393bd1ef583124b007978d0b16afca7dc5b0561cd2a","observation_id":"7c1e4f1f-6ec4-4a5d-9095-ff8c963c2429","resolution":{"observed_at":"2026-05-22T00:05:47.444984Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Overview of the EHRSQL 2024 shared task on reliable text-to-SQL modeling","venue":null,"work_id":"87ff1f45-9202-4d67-be1c-b5b87371eaa6","year":2024},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:bdb03db988dc1e94d7d8527bfbf30e901e79b2211b55190d35b1353a33d0b26b","observation_id":"8fefdd03-4a47-41b5-8252-5f9e89d64bf0","resolution":{"observed_at":"2026-05-22T00:05:47.842636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Sql injection prevention cheat sheet","venue":null,"work_id":"df43a491-fdfc-4bff-b695-b64a41afa031","year":2023},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:97efb771b047efd3728d04d34fe66bfd50b65254a03a71085e1e5671a8fa669a","observation_id":"2ee99a85-60a1-4958-a65a-9f88e657659f","resolution":{"observed_at":"2026-05-22T00:05:47.825057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Model context protocol (mcp) in pharma","venue":null,"work_id":"8030ddfc-0cee-42d6-aeeb-f729ff67fecc","year":2025},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:4bf17e473389d3fcfbad8319e627b714892a37e0a51921b5815d3edef4417ac3","observation_id":"e64e63a1-2c9b-49df-8685-b91ace5faf92","resolution":{"observed_at":"2026-05-22T00:05:47.808156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Future of industrial automation: Trends and predictions for mcp server adoption in smart manufacturing","venue":null,"work_id":"f94f1945-6d40-4925-bded-9cecb30d5f3b","year":null},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:22c5f95411f605f82bc998b23d1fe9903c96cc6ef0499b75c0161fbffa633cd4","observation_id":"77664f49-457b-4ad2-b40d-180b95d69ab0","resolution":{"observed_at":"2026-05-22T00:05:47.844941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"580ff602-e50f-4bbd-a183-6be7714ab25c","year":2025},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:238637cda5288caa3f8970b1e0a84232beb571106b8bbd1526282f4b5c793b34","observation_id":"c1f77e87-1b31-4638-9b04-7b03f084a43e","resolution":{"observed_at":"2026-05-22T00:05:47.803482Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"EHRSQL-2024 GitHub Repository","venue":null,"work_id":"6265420f-3d33-4169-b1ec-2ff63204b733","year":2024},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:9b6cb7e14223230e19c7378457c1835bb6723ad6dd84eaa0e0cf24c206492867","observation_id":"b46d83a5-c925-421f-b38f-07dbb7ec4181","resolution":{"observed_at":"2026-05-22T00:05:47.805980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.18538","last_updated":"2023-10-27T23:36:14Z","snapshot_observed_at":"2026-07-06T16:39:48.771285Z","submitted_at":"2023-10-27T23:36:14Z","title":"Evaluating Cross-Domain Text-to-SQL Models and Benchmarks","version":1},"cited_work":{"arxiv_id":"2310.18538","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.18538","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Evaluating cross-domain text-to-sql models and benchmarks","venue":null,"work_id":"280fdbfb-c1de-4eb0-a5e4-5a35172c8ece","year":2023},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"cited_paper":"/paper/2310.18538","citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:665a4cf26547813bb1de6765b647361aa0195951872d5e2e5ddf070fb7278a36","observation_id":"29a6ea8e-5b76-4551-adf8-f53574288c4c","resolution":{"observed_at":"2026-05-22T00:05:47.452562Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"AmbigQA: Answering ambiguous open-domain questions","venue":null,"work_id":"eac34290-37dc-41b4-821b-5dfe950c10ee","year":2020},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:2e024c08a877d7d6d10712cafdcf6768394274ccbd0281dfb065a799e48f5f8b","observation_id":"5a486ac8-996e-4621-8017-3f4f97e80c04","resolution":{"observed_at":"2026-05-22T00:05:47.813118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Large language models encode clinical knowledge.Nature, 620(7972):172–180","venue":null,"work_id":"2f16f534-31f9-494f-bcf4-aa853c7dc586","year":2023},"citing_paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis","version":4},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-22T00:05:21.750651Z"},"links":{"citing_paper":"/paper/2507.01053"},"observation_digest":"sha256:463eccfe2028520464cb67a475e298bad93e418677d6039ccb4ee6bb2d2916ae","observation_id":"71350252-fc4d-4080-b835-5d2ab9d34932","resolution":{"observed_at":"2026-05-22T00:05:47.851768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.01053","last_updated":"2026-05-20T07:54:09Z","latest_version":4,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-04T22:55:46.075502Z","submitted_at":"2025-06-27T16:24:17Z","title":"M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":1,"verified_exact":4,"verified_fuzzy":24},"total_outbound_references":31},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 2 inbound Pith citation observations for arXiv:2507.01053."}