{"as_of":"2026-08-08T14:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c98eed10970b8fc9c967905c52cab59183842f7f21279b2026af95bca26717d3","coverage":[{"denominator":109,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T19:41:11.314817Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2608.05993/citation-record","integrity":"/paper/2608.05993/integrity","json":"/paper/2608.05993/citation-record.json","paper":"/paper/2608.05993"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:41:10.673978Z","title":"Werthaim, M","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.673978Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:6107db9395d3fb703381c4b7ba5e2aa19ba4fedbd52b9841ee168547b988ab57","observation_id":"3e9a0309-6c0b-476d-a9b1-d485ab88b9da","resolution":{"observed_at":"2026-08-07T19:41:10.673978Z","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":"2509.11803","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:41:15.412654Z","title":null,"venue":null,"work_id":"e366a8c7-e763-4f9a-af0a-540d38f3baf0","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.681347Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:a946e7971442dc29378043745f616c2ee717930297a81a69beec8339249d52f7","observation_id":"68727ba7-3b9f-4ee1-b861-979f88fb2f44","resolution":{"observed_at":"2026-08-07T19:41:15.420661Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2509.11802","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:41:15.202188Z","title":"Goncharok, A","venue":null,"work_id":"99cb507b-a452-45d1-8634-489a7ebb534e","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.689136Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:444839d09e034a2d1c1e02f8ffcce7927336d4ecaa4f7d9bc39905d3e3e52469","observation_id":"6c21ab2e-f8dc-46ff-b266-72c6e11e22ae","resolution":{"observed_at":"2026-08-07T19:41:15.212353Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.26560","last_updated":"2026-05-26T05:14:33Z","snapshot_observed_at":"2026-08-01T16:50:47.432318Z","submitted_at":"2026-05-26T05:14:33Z","title":"Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline","version":1},"cited_work":{"arxiv_id":"2605.26560","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.26560","snapshot_observed_at":"2026-08-07T19:41:14.976762Z","title":"Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline","venue":"cs.CL","work_id":"ca94fe14-59bb-4ae5-b5d1-9ee69e0b6cf3","year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.694793Z"},"links":{"cited_paper":"/paper/2605.26560","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:88dc67eaadf426eeca6fc03ec1575439517b4099265d34270aae54bc93aaaf90","observation_id":"52aac86d-4800-4fe7-86e4-b7bec2ac7f2f","resolution":{"observed_at":"2026-08-07T19:41:14.982886Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:10.701680Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.701680Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:a087e21ff33ece59b100b348fb972358ff2b342606fcb5ac8e829237714fd063","observation_id":"2bf8d0be-a9d0-4dea-9a46-c6191e1270a1","resolution":{"observed_at":"2026-08-07T19:41:10.701680Z","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":"2026-08-07T19:41:10.707396Z","title":"Aperstein, A","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.707396Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:50fdd6d51d7c46b3246a1bd60055a9de52a0efabfdbb4a5fd45cb779c2279dc5","observation_id":"efb2eb1b-af34-4231-bf2c-ef2f78817f08","resolution":{"observed_at":"2026-08-07T19:41:10.707396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.05006","last_updated":"2025-09-05T11:13:20Z","snapshot_observed_at":"2026-08-07T05:15:59.447770Z","submitted_at":"2025-09-05T11:13:20Z","title":"Do Large Language Models Need Intent? Revisiting Response Generation Strategies for Service Assistant","version":1},"cited_work":{"arxiv_id":"2509.05006","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.05006","snapshot_observed_at":"2026-08-07T19:41:14.942028Z","title":"Do Large Language Models Need Intent? Revisiting Response Generation Strategies for Service Assistant","venue":"cs.CL","work_id":"b0c8ce89-0371-49d4-a6d9-fef4bd3f2db3","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.713507Z"},"links":{"cited_paper":"/paper/2509.05006","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:cd77c58d3dcf4d68f81f71988d972914c49cb43cb8edb31176083c06336cdf16","observation_id":"f4bf0df3-4daa-402e-bcd0-5bd652ab8649","resolution":{"observed_at":"2026-08-07T19:41:14.948287Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2606.03650","last_updated":"2026-06-04T10:01:47Z","snapshot_observed_at":"2026-08-06T12:17:15.155456Z","submitted_at":"2026-06-02T13:41:43Z","title":"CoEval: Ranking Language Models for Custom Tasks Without Labeled Data or Trustworthy Benchmarks","version":2},"cited_work":{"arxiv_id":"2606.03650","doi":null,"metadata_source":"pith","pith_arxiv_id":"2606.03650","snapshot_observed_at":"2026-08-07T19:41:14.918785Z","title":"CoEval: Ranking Language Models for Custom Tasks Without Labeled Data or Trustworthy Benchmarks","venue":"cs.CL","work_id":"76d7a954-9a1b-4c5f-82da-347756d3cdc8","year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.718747Z"},"links":{"cited_paper":"/paper/2606.03650","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:207303a4357b1b14a616dcd9788d2fccbfcaeb97b27e8ecf46d98b6e9b9c4ec9","observation_id":"6d976ba7-c856-4b4b-9b35-c359aabdadd3","resolution":{"observed_at":"2026-08-07T19:41:14.925163Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.7910/dvn/cg9x6d","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:41:11.415365Z","title":"Shapira, A","venue":null,"work_id":"20a29967-5272-4bbc-aed8-54c5567aa513","year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.724403Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:c0eaabf6eb3cffbe4656e28a0d472cfa7283c734aed1989cb16cc1d8587c408c","observation_id":"7d560edf-65d2-4ff7-826a-d3a463f05e97","resolution":{"observed_at":"2026-08-07T19:41:11.423380Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.25601","last_updated":"2026-05-25T08:54:23Z","snapshot_observed_at":"2026-07-06T23:35:31.372756Z","submitted_at":"2026-05-25T08:54:23Z","title":"Toward a Benchmark for Controllable Simulation of Imperfect Students with Large Language Models","version":1},"cited_work":{"arxiv_id":"2605.25601","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.25601","snapshot_observed_at":"2026-08-07T19:41:14.892886Z","title":"Toward a Benchmark for Controllable Simulation of Imperfect Students with Large Language Models","venue":"cs.CL","work_id":"56803a6d-7168-4339-b032-c30de5f47308","year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.729992Z"},"links":{"cited_paper":"/paper/2605.25601","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:abcf652f7ccbb2aad4db96cd58d0fa4460a43780d97f827477ff5b79dc335f8a","observation_id":"c0d6dcb4-30b8-4e9e-b081-0f0dba1e4db5","resolution":{"observed_at":"2026-08-07T19:41:14.899278Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.25502","last_updated":"2026-05-25T07:05:21Z","snapshot_observed_at":"2026-08-01T06:18:32.950663Z","submitted_at":"2026-05-25T07:05:21Z","title":"A Controlled Synthetic Benchmark for Educational Aspect-Based Sentiment Analysis","version":1},"cited_work":{"arxiv_id":"2605.25502","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.25502","snapshot_observed_at":"2026-08-07T19:41:14.852639Z","title":"A Controlled Synthetic Benchmark for Educational Aspect-Based Sentiment Analysis","venue":"cs.CL","work_id":"7035e0f7-ae20-4884-9ce2-8e0a4bdb05c9","year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.735765Z"},"links":{"cited_paper":"/paper/2605.25502","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:5909cd4a2033d6c92b7ced0ed4c695082fa7d86f36ddeffe34210db32e8784f5","observation_id":"879d839f-3c26-40a4-aa2d-db1aa98483fe","resolution":{"observed_at":"2026-08-07T19:41:14.862126Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.04810","last_updated":"2025-09-05T05:17:14Z","snapshot_observed_at":"2026-08-05T05:50:06.588145Z","submitted_at":"2025-09-05T05:17:14Z","title":"Code Review Without Borders: Evaluating Synthetic vs. Real Data for Review Recommendation","version":1},"cited_work":{"arxiv_id":"2509.04810","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.04810","snapshot_observed_at":"2026-08-07T19:41:14.825994Z","title":"Code Review Without Borders: Evaluating Synthetic vs. Real Data for Review Recommendation","venue":"cs.SE","work_id":"4864c3f0-527c-4e8b-9f6b-845374680957","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.742635Z"},"links":{"cited_paper":"/paper/2509.04810","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:214519aa00f8555ecd2e1ffc14707cbb965a16ece62c1aec6f72380a1dcf02ce","observation_id":"c37de55d-897e-4015-9f02-8294bed5291c","resolution":{"observed_at":"2026-08-07T19:41:14.833167Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2603.22459","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:41:14.802947Z","title":"Aperstein, L","venue":null,"work_id":"9f54a5fe-8482-47d6-bad7-1124bf4a6795","year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.758834Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:82a854054e0a37d1da61b4c176f2fba2bc81d11ad4c171b66aa0bbbc9cd5bef5","observation_id":"5118ae5e-26cb-421c-ae38-f9b7d218849c","resolution":{"observed_at":"2026-08-07T19:41:14.810751Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:10.777111Z","title":"Aperstein, Y","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.777111Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:e80409381b23f982b6b4eb186e413c6469e5d1697aaa11fba85e5a6d215de82a","observation_id":"15aa2a27-98b4-4844-93cc-2462280ad2ae","resolution":{"observed_at":"2026-08-07T19:41:10.777111Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.05983","last_updated":"2026-06-04T10:25:31Z","snapshot_observed_at":"2026-08-01T16:31:29.111335Z","submitted_at":"2026-06-04T10:25:31Z","title":"Framing, Judging, Steering: An Assessable Competency Model for Teach-ing Students to Reason With Generative AI","version":1},"cited_work":{"arxiv_id":"2606.05983","doi":null,"metadata_source":"pith","pith_arxiv_id":"2606.05983","snapshot_observed_at":"2026-08-07T19:41:14.536865Z","title":"Framing, Judging, Steering: An Assessable Competency Model for Teach-ing Students to Reason With Generative AI","venue":"cs.AI","work_id":"029307b1-fd54-42d9-ba33-944a8cfb6dc1","year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.782966Z"},"links":{"cited_paper":"/paper/2606.05983","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:ce854e330e79b65d016d1f6d7f81c46190ae42095fb78306218d313de493a703","observation_id":"468ecd26-4216-4d85-9459-6d9452b06740","resolution":{"observed_at":"2026-08-07T19:41:14.549603Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.05617","last_updated":"2025-09-06T06:28:28Z","snapshot_observed_at":"2026-08-06T20:01:49.240685Z","submitted_at":"2025-09-06T06:28:28Z","title":"From Joy to Fear: A Benchmark of Emotion Estimation in Pop Song Lyrics","version":1},"cited_work":{"arxiv_id":"2509.05617","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.05617","snapshot_observed_at":"2026-08-07T19:41:14.504933Z","title":"From Joy to Fear: A Benchmark of Emotion Estimation in Pop Song Lyrics","venue":"cs.CL","work_id":"1ad4f408-dbfa-40fa-a152-081d216bda0b","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.790076Z"},"links":{"cited_paper":"/paper/2509.05617","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:7cd9ba523850443f1bdc9077f5a79b5aec8722edfbea6ed1745c9a0d301f8ece","observation_id":"9e3b532e-6d0e-46f4-adeb-c9a205eecd08","resolution":{"observed_at":"2026-08-07T19:41:14.517861Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.09229","last_updated":"2025-09-11T08:06:02Z","snapshot_observed_at":"2026-08-05T20:44:29.103539Z","submitted_at":"2025-09-11T08:06:02Z","title":"Reading Between the Lines: Classifying Resume Seniority with Large Language Models","version":1},"cited_work":{"arxiv_id":"2509.09229","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.09229","snapshot_observed_at":"2026-08-07T19:41:14.479851Z","title":"Reading Between the Lines: Classifying Resume Seniority with Large Language Models","venue":"cs.CL","work_id":"5d8affef-6afd-4d97-802e-1a3c77b704ed","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.803008Z"},"links":{"cited_paper":"/paper/2509.09229","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:f12bfec612f9fa673809a8621c7d3636afabca2bba28ce10f0e7c5e41335cb3e","observation_id":"7cbbe84b-3a9a-44d6-a9ca-8b097c6e7f84","resolution":{"observed_at":"2026-08-07T19:41:14.485677Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:10.810752Z","title":"Aperstein, E","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.810752Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:5db57084ef23aad15b48e8b65006c5e79099c50fc4cf1f641c1af380515654d0","observation_id":"5e9847fc-c845-4e60-8269-e967a4ba3c4a","resolution":{"observed_at":"2026-08-07T19:41:10.810752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.18451","last_updated":"2025-07-24T14:35:16Z","snapshot_observed_at":"2026-08-06T14:35:28.279366Z","submitted_at":"2025-07-24T14:35:16Z","title":"Generation of Synthetic Clinical Text: A Systematic Review","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.18451","snapshot_observed_at":"2026-08-07T19:41:10.816907Z","title":"Alshaikhdeeb et al","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.816907Z"},"links":{"cited_paper":"/paper/2507.18451","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:814236a008f5fe148110231a4d5c19565a9f6ad0af1e82c2e17240ff06c596a3","observation_id":"0002b0ae-e4e5-4bf3-b715-4ab5a980b54a","resolution":{"observed_at":"2026-08-07T19:41:10.816907Z","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":"2506.16594","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:41:14.421397Z","title":"A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Applica- tion","venue":null,"work_id":"a656003e-4655-4c87-9a65-1803db0d0d2b","year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.823143Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:ab5e6b377f61119a19dd97b3f5b1b4e05887cc8c4ed4e8dbecd04b45730a31a4","observation_id":"763e3f60-25dd-402a-a226-2a7aeb7d251f","resolution":{"observed_at":"2026-08-07T19:41:14.430967Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:10.829108Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.829108Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:80fe1fa561eac1c101b54abb5491125fab0c19d01c3652ce9b95e758e950ef24","observation_id":"cc1ba214-3dc1-4e3d-9bbe-8ec1b78c5c5d","resolution":{"observed_at":"2026-08-07T19:41:10.829108Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.00116","last_updated":"2024-07-02T06:51:09Z","snapshot_observed_at":"2026-07-06T18:38:42.333605Z","submitted_at":"2024-06-27T14:00:11Z","title":"Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.00116","snapshot_observed_at":"2026-08-07T19:41:10.835315Z","title":"Ibrahim, Y","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.835315Z"},"links":{"cited_paper":"/paper/2407.00116","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:e98fd04481b6c58a979f4dd277ef8fe4b219395a0a62ccb6edbac3160539bd05","observation_id":"ebbba384-1791-4550-a742-8e3b4440b999","resolution":{"observed_at":"2026-08-07T19:41:10.835315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.03712","last_updated":"2024-12-09T02:14:08Z","snapshot_observed_at":"2026-07-06T18:26:15.151211Z","submitted_at":"2024-06-06T03:15:13Z","title":"A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.03712","snapshot_observed_at":"2026-08-07T19:41:10.843275Z","title":"Zhou et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.843275Z"},"links":{"cited_paper":"/paper/2406.03712","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:f4ebbf37ca0f6ca8f039d303df62aaff16c565c199542bf79b2503de80b13c6a","observation_id":"129a704c-290f-45fe-9c3b-54aeead73a75","resolution":{"observed_at":"2026-08-07T19:41:10.843275Z","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":"2026-08-07T19:41:10.853395Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.853395Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:275b49b363d7bd9710af3502ad673b494f48ea3a8f95d62168fe8c5364a168ab","observation_id":"3bc5da74-bed7-4ceb-9b76-5f80e4e0e341","resolution":{"observed_at":"2026-08-07T19:41:10.853395Z","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":"2026-08-07T19:41:10.859326Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.859326Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:926de53fe899389c8dd0ff2735c46cc2ba337cb55b753df12c2140059d2f3374","observation_id":"a193402a-cf65-427f-837e-b6d82b5011a7","resolution":{"observed_at":"2026-08-07T19:41:10.859326Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.04073","last_updated":"2025-05-07T02:25:29Z","snapshot_observed_at":"2026-08-07T15:49:21.165152Z","submitted_at":"2025-05-07T02:25:29Z","title":"Natural Language Generation in Healthcare: A Review of Methods and Applications","version":1},"cited_work":{"arxiv_id":"2505.04073","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.04073","snapshot_observed_at":"2026-08-07T19:41:14.118246Z","title":"Natural Language Generation in Healthcare: A Review of Methods and Applications","venue":"cs.CL","work_id":"ae5e42af-2085-46df-b15e-4ada190805bb","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.866048Z"},"links":{"cited_paper":"/paper/2505.04073","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:88a104cd732d9db7aee4dcff2280dcf39ed92fbfd6926802ee23fadbd62d8344","observation_id":"23cd8053-32a7-4e66-b827-a5afce511cf1","resolution":{"observed_at":"2026-08-07T19:41:14.124048Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:10.876384Z","title":"Zeng et al","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.876384Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:3664bb435027647db789df24fc4a8c004e2e74359c08ca9a04f9ae867b756d53","observation_id":"780112c9-af39-4499-85f1-978e9c37ee20","resolution":{"observed_at":"2026-08-07T19:41:10.876384Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.15959","last_updated":"2024-06-28T13:28:08Z","snapshot_observed_at":"2026-08-02T12:29:33.015831Z","submitted_at":"2023-10-24T15:59:43Z","title":"NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.15959","snapshot_observed_at":"2026-08-07T19:41:10.884999Z","title":"Wang et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.884999Z"},"links":{"cited_paper":"/paper/2310.15959","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:ae831e86cb003d961d9cca25ad3bac712cd473f9ac228c9dec0985a7eae27e6b","observation_id":"584761d4-9600-415c-ae6a-78f1691bc6f6","resolution":{"observed_at":"2026-08-07T19:41:10.884999Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00287","last_updated":"2025-01-24T23:46:07Z","snapshot_observed_at":"2026-07-06T16:41:31.021679Z","submitted_at":"2023-11-01T04:37:28Z","title":"Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.00287","snapshot_observed_at":"2026-08-07T19:41:10.891706Z","title":"Xu et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.891706Z"},"links":{"cited_paper":"/paper/2311.00287","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:ee1f26a60df48c82d38cfbe7fb4a7ba4bac2bfdfd8292ba9fffd06ada992fe88","observation_id":"81695e5a-d2e4-4b49-9e68-34a3302a49f6","resolution":{"observed_at":"2026-08-07T19:41:10.891706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12896","last_updated":"2024-10-16T16:12:39Z","snapshot_observed_at":"2026-07-06T19:34:51.296918Z","submitted_at":"2024-10-16T16:12:39Z","title":"A Survey on Data Synthesis and Augmentation for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.12896","snapshot_observed_at":"2026-08-07T19:41:10.897835Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.897835Z"},"links":{"cited_paper":"/paper/2410.12896","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:92a24b8f47c0f5ee0385a8ce52d843933eedd228ecf868b8e51051b681d1b4ed","observation_id":"1c595b67-9bc9-4547-b1fb-48ba9a4ddf90","resolution":{"observed_at":"2026-08-07T19:41:10.897835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00179","last_updated":"2025-03-03T21:05:13Z","snapshot_observed_at":"2026-07-06T17:23:22.706195Z","submitted_at":"2024-01-31T21:14:01Z","title":"De-identification is not enough: a comparison between de-identified and synthetic clinical notes","version":2},"cited_work":{"arxiv_id":"2402.00179","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.00179","snapshot_observed_at":"2026-08-07T19:41:14.020069Z","title":"De-identification is not enough: a comparison between de-identified and synthetic clinical notes","venue":"cs.CL","work_id":"5265b7cd-b982-421e-a46b-4614dbaa04e0","year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.903640Z"},"links":{"cited_paper":"/paper/2402.00179","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:e6b3df5330bb6e150156d10cca2ac6984eef975a1184cf1084e39f5a5bf7a9ce","observation_id":"fdd80fe5-cb7c-47ea-8976-77bc9fb738f4","resolution":{"observed_at":"2026-08-07T19:41:14.025739Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:10.908538Z","title":"Kaabachi, J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.908538Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:60a6ea3f3aebe488dd0d16c44efa6dbebca32636fb3b8fcf443e508bb69a14db","observation_id":"238c8088-aadd-4fd6-b719-f4a2280caa61","resolution":{"observed_at":"2026-08-07T19:41:10.908538Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.02022","last_updated":"2023-06-03T06:42:17Z","snapshot_observed_at":"2026-07-06T15:37:31.493443Z","submitted_at":"2023-06-03T06:42:17Z","title":"ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.02022","snapshot_observed_at":"2026-08-07T19:41:10.914074Z","title":"Yim et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.914074Z"},"links":{"cited_paper":"/paper/2306.02022","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:97fbc2be34dabb44c11b0081bd0758d5e0a64a9feb422a821cf89f15f0ebd8e5","observation_id":"24e62366-429f-418d-99b4-336885dbe4bd","resolution":{"observed_at":"2026-08-07T19:41:10.914074Z","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":"2026-08-07T19:41:10.919641Z","title":"Ben Abacha et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.919641Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:6239e9761a9558132f5a2503ff6a8e1a939ada1cc8b17a8ffbf33357c81f0d09","observation_id":"126d8491-ae4e-462f-8ff1-1f8d968bb222","resolution":{"observed_at":"2026-08-07T19:41:10.919641Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.00333","last_updated":"2022-04-01T10:18:28Z","snapshot_observed_at":"2026-08-05T19:42:24.876616Z","submitted_at":"2022-04-01T10:18:28Z","title":"PriMock57: A Dataset Of Primary Care Mock Consultations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.00333","snapshot_observed_at":"2026-08-07T19:41:10.927497Z","title":"Papadopoulos Korfiatis, F","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.927497Z"},"links":{"cited_paper":"/paper/2204.00333","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:bbcbe92ae266b25ef3d59507f4b4d25939c5b0558bddd79a922c5bcc98894d8f","observation_id":"6de00c35-7eef-4c21-a53b-ff8e60954ebc","resolution":{"observed_at":"2026-08-07T19:41:10.927497Z","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":"2026-08-07T19:41:10.935337Z","title":"Rujas, R","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.935337Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:a69bf77f22e2f0c6e8f7e9fccb2ac99e9a4686451a9ca0080ab2cec0e1a8557d","observation_id":"23996613-b952-4f96-9092-3fbf3fe5e9c7","resolution":{"observed_at":"2026-08-07T19:41:10.935337Z","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":"2026-08-07T19:41:10.945930Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.945930Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:6174aafdaf00fb9adc08b2ff4ea1de578b2bed2bc1f6464890f5e1d151177774","observation_id":"0280856d-1954-4e26-b43a-bac8d464e67f","resolution":{"observed_at":"2026-08-07T19:41:10.945930Z","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":"2026-08-07T19:41:10.951785Z","title":"Gormley, K","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.951785Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:d46c22065e226c297eb91148c341fee94d649be6a3a477b9992767dc2a0bb17d","observation_id":"e99232f0-f84e-4d58-8e8c-ebb3df34deb3","resolution":{"observed_at":"2026-08-07T19:41:10.951785Z","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":"2026-08-07T19:41:10.957428Z","title":"Ritter, S","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.957428Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:9160fab5f37486578b3d4c683d5b1d321c61b561115c2412b992c5163b3f24c8","observation_id":"bf7cfc99-89b2-44cc-886b-2b0d24b571b2","resolution":{"observed_at":"2026-08-07T19:41:10.957428Z","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":"2026-08-07T19:41:10.963938Z","title":"Derczynski, E","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.963938Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:59e18e92f22219b4328845c710795beeaa0573e39153151adb2cfc3a4e8d4e0f","observation_id":"db29ef6f-71ea-47a2-b6c3-047a44bfe6d6","resolution":{"observed_at":"2026-08-07T19:41:10.963938Z","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":"2026-08-07T19:41:10.972105Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.972105Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:bcd443157be2ad2d682b727f5319cea359b1d583b9eb32df68ab2ec48524be78","observation_id":"2e3b25fd-f986-4358-88a4-ba28050e8c0f","resolution":{"observed_at":"2026-08-07T19:41:10.972105Z","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":"2026-08-07T19:41:10.981891Z","title":"Scialom et al","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.981891Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:801cb67b53c38c573b5c5bc7fd935228bf53dea679fa20f5fe24d3d6c3aef0f5","observation_id":"db5ab4af-2823-4f98-8afd-6f177cddaeb3","resolution":{"observed_at":"2026-08-07T19:41:10.981891Z","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":"2026-08-07T19:41:10.991016Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.991016Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:3cfd4db743d36585373fc3219b2b2b5eef9d6e66b2d43a553d0b8f67e7335fd4","observation_id":"2f3ed061-f068-48dc-8f8a-c9387686d2d3","resolution":{"observed_at":"2026-08-07T19:41:10.991016Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.04054","last_updated":"2023-06-15T13:53:05Z","snapshot_observed_at":"2026-07-31T20:21:37.955355Z","submitted_at":"2022-11-08T07:26:45Z","title":"ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.04054","snapshot_observed_at":"2026-08-07T19:41:10.997763Z","title":"Zuluaga-Gomez et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.997763Z"},"links":{"cited_paper":"/paper/2211.04054","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:7d4660ab49719bd9a3b954c3441bf0f1069eba108cfd4f6d0ee65b9b5364c7b4","observation_id":"ec5fc819-1ca0-4f43-b459-2be1d76dfe31","resolution":{"observed_at":"2026-08-07T19:41:10.997763Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.07418","last_updated":"2023-11-13T15:54:30Z","snapshot_observed_at":"2026-07-06T16:46:38.855912Z","submitted_at":"2023-11-13T15:54:30Z","title":"Speech-based Slot Filling using Large Language Models","version":1},"cited_work":{"arxiv_id":"2311.07418","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.07418","snapshot_observed_at":"2026-08-07T19:41:13.944558Z","title":"Speech-based Slot Filling using Large Language Models","venue":"cs.CL","work_id":"874d412f-794f-4d2a-b657-89d9a8f0ce1d","year":2023},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.003284Z"},"links":{"cited_paper":"/paper/2311.07418","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:4bd35d106d047fd5d8320bc5f3c701bfb40898249ec4babfcda121119db6aad0","observation_id":"07bd2739-9aed-4c05-b800-4fce5c07111b","resolution":{"observed_at":"2026-08-07T19:41:13.950261Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:11.009383Z","title":"Kao, K.-F","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.009383Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:ef849f0563353e0175d94a3250277b2f3b14a3c1e2a5831828d99e086caecc11","observation_id":"d962916d-ac2f-40dd-a365-d070f1be4328","resolution":{"observed_at":"2026-08-07T19:41:11.009383Z","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":"2026-08-07T19:41:11.015618Z","title":"Wei et al","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.015618Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:5cc0c6c58e2e6e461e3fadfae13a2c8ca3f06626ac0849b6bccdf8fbf97c0f57","observation_id":"9b0ab782-2a43-4419-a92a-f9c736338c45","resolution":{"observed_at":"2026-08-07T19:41:11.015618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00922","last_updated":"2024-11-07T18:59:30Z","snapshot_observed_at":"2026-07-06T18:24:09.535762Z","submitted_at":"2024-06-03T01:32:52Z","title":"MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00922","snapshot_observed_at":"2026-08-07T19:41:11.020815Z","title":"Li et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.020815Z"},"links":{"cited_paper":"/paper/2406.00922","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:ece12e23373c636baea36b6ab361649d8b1493a2386f8dea9cec7ba5b7ef11e3","observation_id":"b3e616fa-8988-417c-8842-a323a99b515e","resolution":{"observed_at":"2026-08-07T19:41:11.020815Z","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":"2026-08-07T19:41:11.027006Z","title":"Tu et al","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.027006Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:f6983c0ee2aa359592fd2c8f96c4e1ce82a0f0e50f0bb27ecd0ccf6fcdd47270","observation_id":"6f36244d-24f9-4e24-9ed6-ca27d13629de","resolution":{"observed_at":"2026-08-07T19:41:11.027006Z","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":"2026-08-07T19:41:11.031817Z","title":"Markel, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.031817Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:0612ac0c104eb2c77e3eb4899b7b48ef8919594450ac3eabd63a04d4e5ebe3f2","observation_id":"1422beaf-7e96-42da-a30a-72346a5ba84c","resolution":{"observed_at":"2026-08-07T19:41:11.031817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.01555","last_updated":"2024-10-02T13:52:09Z","snapshot_observed_at":"2026-07-06T19:26:02.193680Z","submitted_at":"2024-10-02T13:52:09Z","title":"ACE: A LLM-based Negotiation Coaching System","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.01555","snapshot_observed_at":"2026-08-07T19:41:11.036570Z","title":"Shea et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.036570Z"},"links":{"cited_paper":"/paper/2410.01555","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:ed1efd1e6971ecc6efa927d9217d93bd7f73cfb1c7369158db4ee7e65dad6436","observation_id":"a817da05-5929-4338-abcf-b0d45f890d87","resolution":{"observed_at":"2026-08-07T19:41:11.036570Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19226","last_updated":"2024-11-27T08:50:24Z","snapshot_observed_at":"2026-07-06T18:38:00.379225Z","submitted_at":"2024-06-27T14:51:07Z","title":"Simulating Classroom Education with LLM-Empowered Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19226","snapshot_observed_at":"2026-08-07T19:41:11.042447Z","title":"Zhang et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.042447Z"},"links":{"cited_paper":"/paper/2406.19226","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:4c1a66a65e5acfb3e54a8034761666a3fb1a52337bb1aee2a4b68e21048fadca","observation_id":"b5af236b-9263-466d-ae4c-a43a69154f4e","resolution":{"observed_at":"2026-08-07T19:41:11.042447Z","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":"2026-08-07T19:41:11.048382Z","title":"Holderried et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.048382Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:0d46b29c73ae61a05856242b1f08bd1976b687e97f0a406418573d255df02524","observation_id":"0a907b0a-21cc-4222-9f2b-9d36ae4c1fe6","resolution":{"observed_at":"2026-08-07T19:41:11.048382Z","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":"2026-08-07T19:41:11.053430Z","title":"Johri et al","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.053430Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:04aca66bdd8277350ffaf93659fdad46805741249403231e911034fe55421015","observation_id":"93065c28-bf0f-4a58-90e3-2e65c4f6aea7","resolution":{"observed_at":"2026-08-07T19:41:11.053430Z","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":"2026-08-07T19:41:11.063298Z","title":"Jour- nal of Medical Internet Research, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.063298Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:821d7cfb80f019340e7211ac966a97b5f80f9f37babb0113869846926173e3b4","observation_id":"9ea44d0a-aa42-46ee-ab0e-86bc1819ea0f","resolution":{"observed_at":"2026-08-07T19:41:11.063298Z","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":"2026-08-07T19:41:11.068278Z","title":"JMIR Medical Informatics, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.068278Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:65f841b3ee25cecab3c71ece1702c2fc18ed08928b569f51f637c4851bd3e576","observation_id":"dd82be5c-ab2e-48cd-88de-284a5cfe7afb","resolution":{"observed_at":"2026-08-07T19:41:11.068278Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.06285","last_updated":"2024-08-12T16:49:22Z","snapshot_observed_at":"2026-08-06T13:04:18.099417Z","submitted_at":"2024-08-12T16:49:22Z","title":"Synthetic Patient-Physician Dialogue Generation from Clinical Notes Using LLM","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.06285","snapshot_observed_at":"2026-08-07T19:41:11.073268Z","title":"Das et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.073268Z"},"links":{"cited_paper":"/paper/2408.06285","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:3583048b1bd92341d36ecfa55a294bbf5c9f5ee49b745b6ed90ab06c79408d09","observation_id":"931e32e8-5c50-402c-ad61-4f9d3d5fc4d8","resolution":{"observed_at":"2026-08-07T19:41:11.073268Z","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":"2026-08-07T19:41:11.078280Z","title":"Ben Abacha et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.078280Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:0a67ecc3c0a9f9546ddbed75b7a89c9e06cadb1151e96d8e68ef1778c78f1a38","observation_id":"507e3bc6-8629-4232-a743-7a885508f25e","resolution":{"observed_at":"2026-08-07T19:41:11.078280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.16931","last_updated":"2023-06-29T13:30:41Z","snapshot_observed_at":"2026-07-06T15:48:21.177346Z","submitted_at":"2023-06-29T13:30:41Z","title":"UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?","version":1},"cited_work":{"arxiv_id":"2306.16931","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.16931","snapshot_observed_at":"2026-08-07T19:41:13.826475Z","title":"UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?","venue":"cs.CL","work_id":"e75e334e-46b2-494c-bd0b-433920388110","year":2023},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.085467Z"},"links":{"cited_paper":"/paper/2306.16931","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:afc32abeaa255ec6fa5f72718092e3507ac15de6d971c1be1cfe612cb31cb8c0","observation_id":"fb44dcb8-ee8b-4850-8e85-c147527c95ff","resolution":{"observed_at":"2026-08-07T19:41:13.845689Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.11716","last_updated":"2025-06-07T02:01:42Z","snapshot_observed_at":"2026-07-06T20:07:41.462631Z","submitted_at":"2024-12-16T12:36:47Z","title":"LLMs Can Simulate Standardized Patients via Agent Coevolution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.11716","snapshot_observed_at":"2026-08-07T19:41:11.091609Z","title":"Du et al","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.091609Z"},"links":{"cited_paper":"/paper/2412.11716","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:216f233185da890f77a6e58ba9ad3a666d3e53dedd7af046e4eee5e4fdc63b11","observation_id":"3a30233f-dc47-4ab7-af8c-1e9a6ae38f25","resolution":{"observed_at":"2026-08-07T19:41:11.091609Z","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":"2026-08-07T19:41:11.099171Z","title":"Kang et al","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.099171Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:9d297d60707b9823f605b117425dad455dabea253db87c763175444794ce96d7","observation_id":"e5fb0573-32bf-4e7d-8bba-8f95e2a9f4c2","resolution":{"observed_at":"2026-08-07T19:41:11.099171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.07549","last_updated":"2026-04-20T14:34:57Z","snapshot_observed_at":"2026-07-06T22:55:46.307881Z","submitted_at":"2026-04-08T19:52:51Z","title":"EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.07549","snapshot_observed_at":"2026-08-07T19:41:11.104487Z","title":"Ge et al","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.104487Z"},"links":{"cited_paper":"/paper/2604.07549","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:90829dff010465d065cf158f7d82bbcd6d79d522af01192996e8730b545f7ca2","observation_id":"b0a6b0d3-56f1-4e7e-b602-0c823ec3cc07","resolution":{"observed_at":"2026-08-07T19:41:11.104487Z","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":"2510.21228","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:41:13.528772Z","title":"BMC Emergency Medicine (arXiv:2510.21228), 2026","venue":null,"work_id":"22ea9697-0236-4373-8f73-069f5935e6f7","year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.109929Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:be1d03de43de6604388f28cda78816010214cca4ed07914744c1dd6df2942411","observation_id":"ebdbf767-6e50-49e2-b0d5-191af4cc38f4","resolution":{"observed_at":"2026-08-07T19:41:13.537703Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:11.114722Z","title":"Prehospital and Disaster Medicine (PubMed 39675178), 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.114722Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:e578d8569b7b2c966453de3542c83f94d207cd30b5003ec273247c55c6547f11","observation_id":"5dcd8e37-30aa-43cb-b6e5-f504bb80957b","resolution":{"observed_at":"2026-08-07T19:41:11.114722Z","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":"2026-08-07T19:41:15.982984Z","title":"Hartman et al","venue":null,"work_id":"c9b44a93-f837-429c-b127-5eb1e7cc9e4b","year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.119482Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:b58a15f476af0e15fd01a0943d49fbacc7b9bf84255ccd0ab4190bd91c4631e9","observation_id":"9dfc51fc-5a86-42e9-8d32-1849fe395f60","resolution":{"observed_at":"2026-08-07T19:41:15.988075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.06549","last_updated":"2024-11-10T18:06:55Z","snapshot_observed_at":"2026-07-06T19:48:10.800324Z","submitted_at":"2024-11-10T18:06:55Z","title":"In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.06549","snapshot_observed_at":"2026-08-07T19:41:11.123886Z","title":"Gatto et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.123886Z"},"links":{"cited_paper":"/paper/2411.06549","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:d9c8848fe886ead0552d5b9e9911e05925296b2dc016d4376c24866927be3a6e","observation_id":"c5a0ad68-9503-4991-8d5c-5da2e8faf9dd","resolution":{"observed_at":"2026-08-07T19:41:11.123886Z","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":"2026-08-07T19:41:15.947371Z","title":"JAMIA, 32(6):1032, 2025","venue":null,"work_id":"816a95f8-cd31-419f-a2bb-9be40c836518","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.129523Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:440b732c7969cbd5c2347989fe4f0f8ca0065fa97110e764d558f8f1c8b0cd96","observation_id":"f96757f7-b54f-44df-8561-291402e17b3f","resolution":{"observed_at":"2026-08-07T19:41:15.958765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2509.07188","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:41:13.212699Z","title":"Yao et al","venue":null,"work_id":"3c425706-f1df-4a5a-8897-0794e515808c","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.134530Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:58af541ed9b2e7484046d7135d8f03b3704b1b9bb3b58fbda129fb2439f4f307","observation_id":"15346867-f368-4116-ab5d-9d8e127ce579","resolution":{"observed_at":"2026-08-07T19:41:13.221171Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.16603","last_updated":"2024-09-25T04:02:54Z","snapshot_observed_at":"2026-07-06T19:21:33.180652Z","submitted_at":"2024-09-25T04:02:54Z","title":"Overview of the First Shared Task on Clinical Text Generation: RRG24 and \"Discharge Me!\"","version":1},"cited_work":{"arxiv_id":"2409.16603","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.16603","snapshot_observed_at":"2026-08-07T19:41:13.024295Z","title":"Overview of the First Shared Task on Clinical Text Generation: RRG24 and \"Discharge Me!\"","venue":"cs.CL","work_id":"32e61f95-f1b3-47a2-8d5d-795bfe7d3180","year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.139922Z"},"links":{"cited_paper":"/paper/2409.16603","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:4f7c1fa8022a046d3f7c121bcbb11ac56967755b4ae1a59129025154b132d03e","observation_id":"41300fc8-2421-4071-a228-22e13f32666c","resolution":{"observed_at":"2026-08-07T19:41:13.030638Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.17672","last_updated":"2024-11-26T18:31:14Z","snapshot_observed_at":"2026-07-06T19:57:28.746485Z","submitted_at":"2024-11-26T18:31:14Z","title":"Synthetic Data Generation with LLM for Improved Depression Prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.17672","snapshot_observed_at":"2026-08-07T19:41:11.145335Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.145335Z"},"links":{"cited_paper":"/paper/2411.17672","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:4c2ec5a816f9c5881461da962c44e7d71d569e87c8689cba837150e21e21683b","observation_id":"2061bb0a-aff5-4dda-a5c7-d95dd5d014d3","resolution":{"observed_at":"2026-08-07T19:41:11.145335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06056","last_updated":"2025-09-06T21:54:35Z","snapshot_observed_at":"2026-08-04T23:35:12.635946Z","submitted_at":"2024-06-10T07:03:36Z","title":"Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text","version":3},"cited_work":{"arxiv_id":"2406.06056","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.06056","snapshot_observed_at":"2026-08-07T19:41:12.979210Z","title":"Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text","venue":"cs.CL","work_id":"76b1de9a-5685-4f28-a855-f05c5a8c2234","year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.150535Z"},"links":{"cited_paper":"/paper/2406.06056","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:321716de7d19b92aa36be35b887b431e45b677a2c50a8f90866545e5dfd7cbdd","observation_id":"e7db0617-9bc9-407c-aa48-3dfef759988f","resolution":{"observed_at":"2026-08-07T19:41:12.986951Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09742","last_updated":"2024-06-28T03:11:48Z","snapshot_observed_at":"2026-08-06T16:34:49.000585Z","submitted_at":"2024-02-15T06:46:48Z","title":"AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09742","snapshot_observed_at":"2026-08-07T19:41:11.155973Z","title":"Fan et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.155973Z"},"links":{"cited_paper":"/paper/2402.09742","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:1001ee3f3d48b59860afc315f31d3c40d188af86c45897b9f22e0cfb42012238","observation_id":"44d2cc88-936b-4941-b2e6-da3bf6ef964a","resolution":{"observed_at":"2026-08-07T19:41:11.155973Z","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":"2026-08-07T19:41:15.923667Z","title":"Louie et al","venue":null,"work_id":"0280af39-04b9-46e5-9a29-806ff7436416","year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.162166Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:e66f354e0b8393216e6aec1d42986d2cbac0ccd825bf8ff5a912b344eaf3c856","observation_id":"9ff609b8-e6e5-4aa6-a446-1a8512493fbc","resolution":{"observed_at":"2026-08-07T19:41:15.929578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.06715","last_updated":"2025-07-09T10:13:38Z","snapshot_observed_at":"2026-08-06T18:54:07.850409Z","submitted_at":"2025-07-09T10:13:38Z","title":"CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.06715","snapshot_observed_at":"2026-08-07T19:41:11.167873Z","title":"Keerthana, M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.167873Z"},"links":{"cited_paper":"/paper/2507.06715","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:b9696eed82fd0078842305cd052c612ab7eb0cc7742c43265ee282c7afee1350","observation_id":"b87b4bc1-79df-49ea-8468-3747e88c6c98","resolution":{"observed_at":"2026-08-07T19:41:11.167873Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.17717","last_updated":"2026-06-09T20:25:14Z","snapshot_observed_at":"2026-08-03T08:15:07.553014Z","submitted_at":"2026-01-25T06:40:25Z","title":"A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.17717","snapshot_observed_at":"2026-08-07T19:41:11.174610Z","title":"Zhang, M","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.174610Z"},"links":{"cited_paper":"/paper/2601.17717","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:ae48904c2024f406b45230a26c55004beba53b0cd5a8fc8a79c9897eed474a22","observation_id":"fb1070a7-6aaf-4b69-a540-d5d7641ac9e8","resolution":{"observed_at":"2026-08-07T19:41:11.174610Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13919","last_updated":"2024-10-03T02:54:46Z","snapshot_observed_at":"2026-07-06T17:33:29.707531Z","submitted_at":"2024-02-21T16:33:22Z","title":"SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization","version":4},"cited_work":{"arxiv_id":"2402.13919","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.13919","snapshot_observed_at":"2026-08-07T19:41:12.892281Z","title":"SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization","venue":"cs.CL","work_id":"5aaf0d65-7f3c-4e6a-aa83-1977b9aeb196","year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.179853Z"},"links":{"cited_paper":"/paper/2402.13919","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:a4d5323cf3789e78e020ee8954594838df856a8fadde392ebb87a20388011f93","observation_id":"1e51b053-ff4a-43b1-bc9a-949f31c6ae79","resolution":{"observed_at":"2026-08-07T19:41:12.898668Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:11.186035Z","title":"arXiv:2502.14921, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.186035Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:577636ee26aa346065b161e8f8660743869776835ee89fa05983c7148d3e0f51","observation_id":"106f235a-4ec2-4cc9-b6cf-355a118293b1","resolution":{"observed_at":"2026-08-07T19:41:11.186035Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.20452","last_updated":"2025-08-28T05:57:47Z","snapshot_observed_at":"2026-08-06T16:29:22.774856Z","submitted_at":"2025-08-28T05:57:47Z","title":"Evaluating Differentially Private Generation of Domain-Specific Text","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.20452","snapshot_observed_at":"2026-08-07T19:41:11.190542Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.190542Z"},"links":{"cited_paper":"/paper/2508.20452","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:b0337b3c596447ef5d168f55cd85a52a6ad837b444fa7c8388bc0f146fd445f0","observation_id":"76051540-3b81-48e2-a435-6e92bf791990","resolution":{"observed_at":"2026-08-07T19:41:11.190542Z","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":"2026-08-07T19:41:15.905533Z","title":"Nayak et al","venue":null,"work_id":"272f97cd-e29f-4b42-b6db-0571dfe5c5fe","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.195341Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:8e530b2341d6cdca7aa630ec0224a0c6f89b935e1131dfea2e5b4e50a6f0da0a","observation_id":"394536fd-2d3f-483e-8bbe-20100fe8b95f","resolution":{"observed_at":"2026-08-07T19:41:15.911832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.27014","last_updated":"2026-04-29T11:32:59Z","snapshot_observed_at":"2026-07-06T23:12:33.898150Z","submitted_at":"2026-04-29T11:32:59Z","title":"Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentation","version":1},"cited_work":{"arxiv_id":"2604.27014","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.27014","snapshot_observed_at":"2026-08-07T19:41:12.620671Z","title":"Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentation","venue":"cs.LG","work_id":"8ba7e120-3c75-4109-ac52-ee51e612879d","year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.199896Z"},"links":{"cited_paper":"/paper/2604.27014","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:b32fc8fa51a5285f3a8b32600f23b8efd69b638fff443bd1adb389912ce61cc8","observation_id":"89ee9055-0ff1-40bd-8535-fd238f29cd03","resolution":{"observed_at":"2026-08-07T19:41:12.627316Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.22450","last_updated":"2025-05-28T15:10:33Z","snapshot_observed_at":"2026-08-07T22:06:57.196350Z","submitted_at":"2025-05-28T15:10:33Z","title":"Position: All Current Generative Fidelity and Diversity Metrics are Flawed","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.22450","snapshot_observed_at":"2026-08-07T19:41:11.205425Z","title":"Räisä, B","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.205425Z"},"links":{"cited_paper":"/paper/2505.22450","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:5ab3da3ab52af4d4f6363ff6fa5bc3863f3e2de3792ed7c150b1342e80bdc258","observation_id":"7fb88898-3301-430e-bede-2993007d6b19","resolution":{"observed_at":"2026-08-07T19:41:11.205425Z","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":"2026-08-07T19:41:15.882677Z","title":"Asgari, N","venue":null,"work_id":"56e0727b-9f42-426a-8140-4c9cfdf38a49","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.210589Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:2dc0d7a41ca525053efd942c4f6ffb54914a9b4634cbbfe753a15435584a3c49","observation_id":"ed66baf3-73f2-4d32-9558-2ff35656684d","resolution":{"observed_at":"2026-08-07T19:41:15.891981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2505.03025","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:41:12.564677Z","title":"Bedrick, A","venue":null,"work_id":"4e4a7954-e3df-4562-9848-22fef5babd81","year":2026},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.215205Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:25dd07963c8a041a6018f9516ffa1fd41001218747b097737a1512794d6e46a3","observation_id":"718cdd0c-e3a4-4c84-aa84-92a259be3f1d","resolution":{"observed_at":"2026-08-07T19:41:12.579155Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:15.861497Z","title":null,"venue":null,"work_id":"72005839-0b52-40c5-b864-62f8654a1a17","year":1975},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.220041Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:13c6a9d1eb5a22d0feb308e89cd63a2577bbed3a7acfb394f595d980fb3b88b7","observation_id":"7003e803-bc42-4614-98d7-2b3f15d97d91","resolution":{"observed_at":"2026-08-07T19:41:15.868261Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:15.839111Z","title":"Malpractice Risks in Communication Fail- ures: 2015 Annual Benchmarking Report","venue":null,"work_id":"4e5984a9-0dc4-43a8-9576-7676180b7b88","year":2015},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.224969Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:f11a4de4cff7493caf8fed57fd38a172e38a450da2003b2618147a3f103a16ff","observation_id":"f7e887ac-bea4-4ed5-bb80-c29e8ed6b2fc","resolution":{"observed_at":"2026-08-07T19:41:15.845506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:15.764888Z","title":"Sentinel Event Data Summary (annual root-cause reports)","venue":null,"work_id":"2e00e389-9e96-475f-9753-e86cce5afaa7","year":null},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.235011Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:09fa7584d0c11a8cf2872d335d47fb855a5c9e990674d9bcc7ddc3c6a86f8dbc","observation_id":"24536ad2-f8e3-44ff-83b9-27b9ddb2398e","resolution":{"observed_at":"2026-08-07T19:41:15.770934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:15.748681Z","title":"Iedema et al","venue":null,"work_id":"c2e33e61-90a3-4e6a-9fec-2e111faf298d","year":null},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.240899Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:78a3d304c52af033ba65e99aa7bc9ea033ce0ac2f9de375cf83bed4f9e7a9521","observation_id":"37af0d37-510e-480f-a28b-cb52e4dbeaf7","resolution":{"observed_at":"2026-08-07T19:41:15.754631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:15.728359Z","title":null,"venue":null,"work_id":"759addcf-9203-438b-b8f1-0fec675652b0","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.249711Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:d2b550d8d4826e67f7d772c4225963e23b7d758afd21bbc2d0fcb09044fd6171","observation_id":"a7c2ff50-5354-4bef-ad5f-cd4e98c85598","resolution":{"observed_at":"2026-08-07T19:41:15.735133Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:15.707312Z","title":"Nath et al","venue":null,"work_id":"31189235-6013-4813-aa0e-29c514a86fd4","year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.257039Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:f42ca07d39f5629fdc65c88562112ba6f69a318e444872f1b48ad795753c5f53","observation_id":"08f92a8d-7e17-457c-8ac4-20075dbe515c","resolution":{"observed_at":"2026-08-07T19:41:15.712569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:15.684424Z","title":"Joshi, K","venue":null,"work_id":"b86cd248-c24d-4e7a-bb7b-74b773a5506f","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.263118Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:1f02bccd184f97da1459ce619357a22840993e29ee70aeb83bf53cd5d32a96c9","observation_id":"6acf7eb8-ff75-4aac-b0bf-b4f766f87eaf","resolution":{"observed_at":"2026-08-07T19:41:15.691224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:15.659255Z","title":null,"venue":null,"work_id":"2eb0c1dc-f2e6-4f0b-81e6-0285f6edc11f","year":2019},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.268919Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:b21e44feed2afc770739b509d51a33c395761cf02ff1b6ac581411575ac2c314","observation_id":"27d10ab5-9803-4323-a912-6f1487814f70","resolution":{"observed_at":"2026-08-07T19:41:15.666842Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:15.632554Z","title":null,"venue":null,"work_id":"0f569dd4-f5d0-477e-b473-713615d02c34","year":2021},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.274676Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:8dc0f84ea033ea23d922bef9a64099e669d2ba87409bc02f68a6103b4feb501d","observation_id":"40965995-d799-466b-b46e-422ca8f3a959","resolution":{"observed_at":"2026-08-07T19:41:15.638761Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:15.614618Z","title":null,"venue":null,"work_id":"6b95e514-496c-4f32-8839-ff999ed6e316","year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.280504Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:889f9121aac57b462a4106e42548d35344b2ca4b378652dc2d4cfe6b31c47081","observation_id":"25b02144-2649-4cc5-99b4-9895419932be","resolution":{"observed_at":"2026-08-07T19:41:15.620222Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03596","last_updated":"2020-07-07T16:32:44Z","snapshot_observed_at":"2026-08-04T14:16:19.131793Z","submitted_at":"2020-07-07T16:32:44Z","title":"An Emergency Medical Services Clinical Audit System driven by Named Entity Recognition from Deep Learning","version":1},"cited_work":{"arxiv_id":"2007.03596","doi":null,"metadata_source":"pith","pith_arxiv_id":"2007.03596","snapshot_observed_at":"2026-08-07T19:41:12.362480Z","title":"An Emergency Medical Services Clinical Audit System driven by Named Entity Recognition from Deep Learning","venue":"cs.CL","work_id":"fac8153b-fdfe-4d53-8b28-dd142675fe17","year":2020},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.285208Z"},"links":{"cited_paper":"/paper/2007.03596","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:bc352026337117f8965569e5af197ba9f3b9d712d0c24c2975451d593eeeb38c","observation_id":"e8543c87-bf24-4928-b33a-be6b60ec8b2e","resolution":{"observed_at":"2026-08-07T19:41:12.369678Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:15.592136Z","title":"Wang et al","venue":null,"work_id":"3546ef63-3d06-4029-a4c6-343b46e18f0d","year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.290513Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:99e276eea5672dcf35d4db7d9589485ddc60e82717c930c071e538cedf9bb601","observation_id":"a0a1ba6d-4d57-47fc-b70b-717d7e17a61f","resolution":{"observed_at":"2026-08-07T19:41:15.600946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:15.565416Z","title":null,"venue":null,"work_id":"5b0f5d87-310e-4c1e-9f2b-849eef0ce0da","year":2023},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.295142Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:0eb35868a77226827f1fd981e38fe78ff8730ba2bfeb0da715d3529e1e6f62a6","observation_id":"35e66506-03ec-4cab-81a4-eb86f5cba1f2","resolution":{"observed_at":"2026-08-07T19:41:15.573658Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T19:41:15.535878Z","title":null,"venue":null,"work_id":"b323c9d6-98eb-4207-b0c7-0dd11a56f80c","year":2017},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.299965Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:0f1347ee34be1d0e1ae251bb284044fa304af98ac527fda8a049d2fba0954597","observation_id":"7b6c10f0-15e0-4fba-9c5c-f0eaf729baf9","resolution":{"observed_at":"2026-08-07T19:41:15.542305Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.19163","last_updated":"2025-08-26T16:12:12Z","snapshot_observed_at":"2026-08-08T02:07:41.489412Z","submitted_at":"2025-08-26T16:12:12Z","title":"MATRIX: Multi-Agent simulaTion fRamework for safe Interactions and conteXtual clinical conversational evaluation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.19163","snapshot_observed_at":"2026-08-07T19:41:11.304560Z","title":"Lim et al","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.304560Z"},"links":{"cited_paper":"/paper/2508.19163","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:b54f3dd80f3613f7bd5074ec3c9fb0c33f7ba9d49a01471e196e92281bd073c7","observation_id":"97bdfa92-0997-4275-a444-a32ee276534c","resolution":{"observed_at":"2026-08-07T19:41:11.304560Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.07960","last_updated":"2025-05-25T02:19:37Z","snapshot_observed_at":"2026-07-30T08:34:47.046912Z","submitted_at":"2024-05-13T17:38:53Z","title":"AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07960","snapshot_observed_at":"2026-08-07T19:41:11.309786Z","title":"Schmidgall et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.309786Z"},"links":{"cited_paper":"/paper/2405.07960","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:1abb3eafad75a833980a5d6cf71087b17016c7a5c3e852da15acab73c9c9fca0","observation_id":"da53adc7-f818-424b-8621-910bb7be8891","resolution":{"observed_at":"2026-08-07T19:41:11.309786Z","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":"2026-08-07T19:41:11.314817Z","title":"Qin et al","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:11.314817Z"},"links":{"citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:372205db5fe072dd2435943492d40b7908f11164f353523f19003c75d61cd922","observation_id":"0deb7402-7637-4294-9e6a-dbf1f047ab44","resolution":{"observed_at":"2026-08-07T19:41:11.314817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-08T13:16:42.804386Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":63,"verified_exact":26,"verified_fuzzy":11},"total_outbound_references":109},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 100 of 109 outbound references and 0 inbound Pith citation observations for arXiv:2608.05993."}