{"as_of":"2026-08-10T13:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:08bf5f3d0f045ef95dec92cc9d6287d469903f88be42f8abcabe6b06c89b74f4","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:10:46.701581Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2506.19217/citation-record","integrity":"/paper/2506.19217/integrity","json":"/paper/2506.19217/citation-record.json","paper":"/paper/2506.19217"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:51.480811Z","title":"Computed tomography and magnetic resonance imaging: past, present and future.European Respiratory Journal, 19(35 suppl):3s–12s, 2002","venue":null,"work_id":"133ac65d-cea8-4902-aa2f-5911d0aeab8b","year":2002},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:42.598520Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:7abf4d6d38ef3608429f400242dd018822678449a62204e4bdc68e147bbe8a7f","observation_id":"0cbf479e-e58c-4054-a24a-01bfb744648e","resolution":{"observed_at":"2026-08-06T23:10:51.485949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:51.464199Z","title":"Should we be concerned about the rapid increase in ct usage?Reviews on environmental health, 25(1):63–68, 2010","venue":null,"work_id":"9ff55a8c-be28-4b8d-b252-07665f6ff9a9","year":2010},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:42.647398Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:eaed516be572fe7ff73d1475d6e6f6c2cbb9e2e83d77ff5020519891d9a2ae32","observation_id":"9e6857e0-9f0d-4a9e-b64b-987df1fcd7a5","resolution":{"observed_at":"2026-08-06T23:10:51.469459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:51.448346Z","title":"Cognitive and system factors contribut- ing to diagnostic errors in radiology.American Journal of Roentgenology, 201(3):611–617, 2013","venue":null,"work_id":"6b841a35-2ec5-48e7-80c1-4e7e973423c7","year":2013},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:42.739674Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:c59fa0d0815d04d37293195f35beae6a404befd43bb5089dc5df7472dee26a33","observation_id":"1786067b-301c-4b76-864b-451275c3d7a2","resolution":{"observed_at":"2026-08-06T23:10:51.454129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:51.428999Z","title":"Automated radiology report generation: A review of recent advances.IEEE Reviews in Biomedical Engineer- ing, 2024","venue":null,"work_id":"24d9e873-1e68-458c-8eb5-b11a1b156cb1","year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:42.826120Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:13d64f0fb19a00d22ffae3948855180235dfa2c07d0bf35faa24144e24e694b5","observation_id":"2a811af5-e706-4dc4-974a-dacc0cbcc068","resolution":{"observed_at":"2026-08-06T23:10:51.433752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:51.412294Z","title":"Comparing diagnostic accuracy of radiolo- gists versus gpt-4v and gemini pro vision using image inputs from diagnosis please cases.Radiology, 312(1), July 2024","venue":null,"work_id":"758318b5-e9be-4d0d-8e86-3af1b5010315","year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:42.915903Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:4c4d733478da26434e9947f5dd61cc4b64af7d46c90c03a34134ccdffc594c33","observation_id":"6ef77ae9-f7cf-4339-bb01-206237bc6697","resolution":{"observed_at":"2026-08-06T23:10:51.417716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:51.322946Z","title":"Evaluating large language models on medical evidence summarization.NPJ digital medicine, 6(1):158, 2023","venue":null,"work_id":"9818069f-f09b-402d-97dd-a71015d13ca0","year":2023},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:43.011449Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:1fc7ff789817d2a30e2699ae392ddace6ebee676d6aa842658b4dfae29176463","observation_id":"9ce878c1-d2f8-4c0f-a405-be2a867a5b76","resolution":{"observed_at":"2026-08-06T23:10:51.368002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:51.227490Z","title":"Embracing large language models for medical applications: opportuni- ties and challenges.Cureus, 15(5), 2023","venue":null,"work_id":"f33e134f-c4ca-4288-b4ea-93ec30a8410a","year":2023},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:43.097684Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:f89c4af80218be8f1218077ca3feb73b1b1c06b3bf61e0f9de8014803ed1f9de","observation_id":"e1bfba31-07e9-48d4-aa5e-dd1b23790d72","resolution":{"observed_at":"2026-08-06T23:10:51.234294Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:50.970320Z","title":"A dataset of clinically generated visual questions and answers about radiology images.Scientific data, 5(1):1–10, 2018","venue":null,"work_id":"18669c62-82dd-4b68-90b6-6f6f7fb43298","year":2018},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:43.193737Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:95669aa0bfcca0484a179c65d818f9df6611f9400e7fa36e8c99ae8ff79d0972","observation_id":"a1cfe936-bb90-4e1f-9b69-6e983c5c4bdd","resolution":{"observed_at":"2026-08-06T23:10:51.108888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.10286","last_updated":"2020-03-07T17:55:41Z","snapshot_observed_at":"2026-07-06T09:06:42.071993Z","submitted_at":"2020-03-07T17:55:41Z","title":"PathVQA: 30000+ Questions for Medical Visual Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.10286","snapshot_observed_at":"2026-08-06T23:10:43.293038Z","title":"Pathvqa: 30000+ questions for medical vi- sual question answering.arXiv preprint arXiv:2003.10286, 2020","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:43.293038Z"},"links":{"cited_paper":"/paper/2003.10286","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:641f00fb1b0883d5a2728ae83b80ff028f8f83894da66403f55f51c868f41193","observation_id":"edb9e078-c00c-4196-aa6c-2063a1b601e6","resolution":{"observed_at":"2026-08-06T23:10:43.293038Z","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":"2026-08-06T23:10:50.647065Z","title":"Slake: A semantically-labeled knowledge- enhanced dataset for medical visual question answering","venue":null,"work_id":"e4c70e82-3b53-4cb3-a19b-632c08798fc0","year":2021},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:43.381150Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:cd70eefa00e422eef295001627e9ceb081e275fae4569adb2afc5243a5b24125","observation_id":"fb21cd16-96b6-45dd-99f6-433cc62987a8","resolution":{"observed_at":"2026-08-06T23:10:50.786538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10415","last_updated":"2024-09-08T01:04:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-17T17:50:16Z","title":"PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10415","snapshot_observed_at":"2026-08-06T23:10:43.479305Z","title":"Pmc-vqa: Vi- sual instruction tuning for medical visual question answer- ing.arXiv preprint arXiv:2305.10415, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:43.479305Z"},"links":{"cited_paper":"/paper/2305.10415","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:a10bad6a7ffaef5392fe4602f4730eedde33cb4e1ee8544ee3e5bb982464c305","observation_id":"428a250c-52c1-46a2-b798-e1b4bd39fbcc","resolution":{"observed_at":"2026-08-06T23:10:43.479305Z","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":"2026-08-06T23:10:50.359605Z","title":"Omnimedvqa: A new large-scale comprehensive evaluation benchmark for medical lvlm","venue":null,"work_id":"f4e0b543-4b19-4095-bd81-1b632fb83be1","year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:43.555156Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:c84773298221dd8b64c48b3bf7f592de1a567c05cd758cdf1eaa9dde9a284efc","observation_id":"115ddd63-1894-4bef-9a33-8707ebcfab85","resolution":{"observed_at":"2026-08-06T23:10:50.479972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:50.029000Z","title":"Gmai-mmbench: A comprehensive multimodal evaluation benchmark towards general medical ai.Advances in Neural Information Processing Systems, 37:94327–94427, 2024","venue":null,"work_id":"96638283-89db-482d-ab8f-278612fedc25","year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:43.645745Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:3ccd10fb9e356314a9e1b7ffee459decdfac9ba3d3509ba20176d2ccd672032d","observation_id":"88b74f26-1912-41a0-ab9b-ccc68fbf59d6","resolution":{"observed_at":"2026-08-06T23:10:50.210620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.18362","last_updated":"2025-06-06T13:00:07Z","snapshot_observed_at":"2026-08-10T06:07:52.030350Z","submitted_at":"2025-01-30T14:07:56Z","title":"MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.18362","snapshot_observed_at":"2026-08-06T23:10:43.721022Z","title":"Medxpertqa: Benchmarking expert-level medical reasoning and understanding.arXiv preprint arXiv:2501.18362, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:43.721022Z"},"links":{"cited_paper":"/paper/2501.18362","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:b761094d0c0d33910a8a8120ea420d3cd0e051c07097bcd984796835aeac6294","observation_id":"05f1c7c1-6992-4164-925c-2b7b3d5f5976","resolution":{"observed_at":"2026-08-06T23:10:43.721022Z","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":"2026-08-06T23:10:49.642654Z","title":"Mme-survey: A comprehensive survey on evaluation of multimodal llms,","venue":null,"work_id":"1297efad-dc39-4dab-b8df-78b921543e4f","year":null},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:43.818300Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:7d7836bd6244fd7f82272da40be3879321c2f41fb6b257ae205d8d0c3c258800","observation_id":"a8d1fb6e-ba17-4062-b407-3ce167e99ed7","resolution":{"observed_at":"2026-08-06T23:10:49.811271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:49.235581Z","title":"Kim and Liem T","venue":null,"work_id":"40a201eb-9cca-4761-a485-7ba27c0b0dbf","year":2014},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:43.903843Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:b28dc07fdd90843e4b3fd28d5005fd528a87cd4bf45609053968b22b71e0b964","observation_id":"69e2268f-8bd6-4168-b4ab-fcec64620eab","resolution":{"observed_at":"2026-08-06T23:10:49.430981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:48.923935Z","title":"Recovery at the edge of error: debunking the myth of the infallible expert.Journal of biomedical in- formatics, 44(3):413–424, 2011","venue":null,"work_id":"38362578-c76a-47f5-a41f-1f3c391427bf","year":2011},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:43.983906Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:f0f136aec4ef78690b16b48250f217d9c1eafaa789d9d96d8805e16c0b882e45","observation_id":"c25235c2-3a65-424e-9262-fa9a93711142","resolution":{"observed_at":"2026-08-06T23:10:49.078302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:48.662054Z","title":"Overview of the mediqa-corr 2024 shared task on medical error detection and correction","venue":null,"work_id":"26a75f94-82f9-4ad9-ae0c-231b49a7c972","year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:44.067488Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:57f1b88a4363f1962c793c14cf960f6cb4b5eb40d757b198adf3c53869dc289a","observation_id":"e6c3db7a-8e7f-4548-898c-e984a7b7deae","resolution":{"observed_at":"2026-08-06T23:10:48.799582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:48.539922Z","title":"Potential of gpt-4 for detecting errors in radiology reports: implications for report- ing accuracy.Radiology, 311(1):e232714, 2024","venue":null,"work_id":"98cb489d-d22b-481c-b5fe-af8c55c2d647","year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:44.141513Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:87546128f45f880095872fd22934a395203d41669bb29017bb68eb518d0bbf89","observation_id":"06a40d3c-bd45-40a2-a5e0-0983b2bbd85b","resolution":{"observed_at":"2026-08-06T23:10:48.626354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.18619","last_updated":"2024-12-30T03:00:30Z","snapshot_observed_at":"2026-08-09T14:32:31.404668Z","submitted_at":"2024-12-16T05:02:25Z","title":"Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.18619","snapshot_observed_at":"2026-08-06T23:10:44.228630Z","title":"Next token prediction towards multi- modal intelligence: A comprehensive survey.arXiv preprint arXiv:2412.18619, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:44.228630Z"},"links":{"cited_paper":"/paper/2412.18619","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:e7a7bf179cfb000cee7fb1e5d08ad88a1fe9951ae129741aff043d539e7877b7","observation_id":"18e7c4a7-cc8a-4549-9bf3-db27cfb420f7","resolution":{"observed_at":"2026-08-06T23:10:44.228630Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.19330","last_updated":"2024-09-28T12:31:07Z","snapshot_observed_at":"2026-08-09T05:02:05.885998Z","submitted_at":"2024-09-28T12:31:07Z","title":"3D-CT-GPT: Generating 3D Radiology Reports through Integration of Large Vision-Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.19330","snapshot_observed_at":"2026-08-06T23:10:44.308483Z","title":"3d-ct-gpt: Generating 3d radiology reports through integration of large vision-language models.arXiv preprint arXiv:2409.19330, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:44.308483Z"},"links":{"cited_paper":"/paper/2409.19330","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:ade8deccfe37bc8c005ec5f4f9fa8188e0b25962f36f1c05db554728d5e8cd84","observation_id":"83010895-4fda-422d-8ff1-67939e9412c1","resolution":{"observed_at":"2026-08-06T23:10:44.308483Z","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":"2026-08-06T23:10:48.327909Z","title":"Med3dvlm: An efficient vision-language model for 3d med- ical image analysis, 2025","venue":null,"work_id":"571a87f0-a9a4-4586-a87c-d6b9be4f402f","year":2025},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:44.712221Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:d4f791b2884826185c74f96c8f9b8973aed741678545fc9aa190c83cbf253c51","observation_id":"5353d755-11e3-4d34-a00d-9f1d3d9cb799","resolution":{"observed_at":"2026-08-06T23:10:48.447620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:48.221513Z","title":"Medm-vl: What makes a good medical lvlm?,","venue":null,"work_id":"99dba38c-ea68-4a15-9a13-424f4aad7cfb","year":null},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:44.801787Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:52461a80e40746cfe538651631aed300787d36813e0a96973bf3c127a6996d2a","observation_id":"0382bbba-26ec-4f69-8ff6-65e5914ada57","resolution":{"observed_at":"2026-08-06T23:10:48.276586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19260","last_updated":"2025-01-02T18:46:05Z","snapshot_observed_at":"2026-08-10T05:28:04.707921Z","submitted_at":"2024-12-26T15:54:10Z","title":"MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19260","snapshot_observed_at":"2026-08-06T23:10:44.901214Z","title":"Medec: A bench- mark for medical error detection and correction in clinical notes.arXiv preprint arXiv:2412.19260, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:44.901214Z"},"links":{"cited_paper":"/paper/2412.19260","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:0bd68a64f0c53cc22f59bba02b1e705d868dd5a62d9ecce5e8471289b76438ba","observation_id":"17dd16c3-2a9e-4bc6-b893-7ac6135e1c63","resolution":{"observed_at":"2026-08-06T23:10:44.901214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.10829","last_updated":"2024-09-17T01:42:39Z","snapshot_observed_at":"2026-08-03T13:47:41.206814Z","submitted_at":"2024-09-17T01:42:39Z","title":"ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.10829","snapshot_observed_at":"2026-08-06T23:10:45.037970Z","title":"Rao, Serena Zhang, Julian N","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:45.037970Z"},"links":{"cited_paper":"/paper/2409.10829","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:e5010732b5beeabb29b210e5c3921973515cdd99b4538a0e8e462bdfae77b3b2","observation_id":"33204f4a-b552-4226-9903-d3757868ced9","resolution":{"observed_at":"2026-08-06T23:10:45.037970Z","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":"2026-08-06T23:10:48.100311Z","title":"Mimic-cxr, a de- identified publicly available database of chest radiographs with free-text reports.Scientific data, 6(1):317, 2019","venue":null,"work_id":"1906e8bc-4269-428d-986e-9d3163e534a7","year":2019},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:45.146921Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:2e7489075e21e6635c1a75777cbfbdbb3249611c9a70a3ecfae9209f1d71bda1","observation_id":"44d7a051-144e-4d14-950f-827898976d04","resolution":{"observed_at":"2026-08-06T23:10:48.149757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.02730","last_updated":"2024-07-03T00:59:03Z","snapshot_observed_at":"2026-08-10T02:15:15.378395Z","submitted_at":"2024-07-03T00:59:03Z","title":"MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.02730","snapshot_observed_at":"2026-08-06T23:10:45.285853Z","title":"MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context, July 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:45.285853Z"},"links":{"cited_paper":"/paper/2407.02730","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:1d87455600081ff36d358652bf055348e220130f56f35d2c3d223ff5d48a89ce","observation_id":"63cfcc53-e338-4855-aab8-75092ce45f50","resolution":{"observed_at":"2026-08-06T23:10:45.285853Z","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":"2026-08-06T23:10:47.978434Z","title":"A foundation model utilizing chest ct volumes and radiology reports for supervised-level zero- shot detection of abnormalities.CoRR, 2024","venue":null,"work_id":"2ee95195-5570-4dad-8956-2d4203f78efd","year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:45.400446Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:15b5e2fdef29761274deec50484b9ad17adf729803fc46fc518ae716e63eb83a","observation_id":"bdb16b3b-681a-4534-a31f-e49f2746f9b5","resolution":{"observed_at":"2026-08-06T23:10:48.034543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.16754","last_updated":"2024-04-25T17:11:37Z","snapshot_observed_at":"2026-08-07T21:48:29.327261Z","submitted_at":"2024-04-25T17:11:37Z","title":"RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.16754","snapshot_observed_at":"2026-08-06T23:10:45.482676Z","title":"Radgenome-chest ct: A grounded vision-language dataset for chest ct analysis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:45.482676Z"},"links":{"cited_paper":"/paper/2404.16754","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:efe9d9e7e2ed27d94a03343a1ea9512bd50b221bc73f2de8f1c70be311e64bd4","observation_id":"4f65962b-c11c-4f1c-b76e-b002564a7ca5","resolution":{"observed_at":"2026-08-06T23:10:45.482676Z","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":"2026-08-06T23:10:47.852583Z","title":"Cambrian-1: A fully open, vision-centric explo- ration of multimodal llms.Advances in Neural Information Processing Systems, 37:87310–87356, 2024","venue":null,"work_id":"190df897-1bc0-4998-a4c3-dae9b5f150e4","year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:45.564153Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:1ea8b0b7f405f1f2701bb663000dcf1d5d97ec901ca8b8c5b64eb99a54507f6d","observation_id":"6f7c1a52-eae0-465f-8324-1dfff9514139","resolution":{"observed_at":"2026-08-06T23:10:47.904119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.03714","last_updated":"2023-10-05T17:37:25Z","snapshot_observed_at":"2026-08-04T05:21:05.846165Z","submitted_at":"2023-10-05T17:37:25Z","title":"DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03714","snapshot_observed_at":"2026-08-06T23:10:45.655074Z","title":"Dspy: Compiling declarative language model calls into self-improving pipelines.arXiv preprint arXiv:2310.03714,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:45.655074Z"},"links":{"cited_paper":"/paper/2310.03714","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:ad168cd54f6e8647be5c26df29a88c944641990672835004b74dca35b45c095c","observation_id":"99978041-19d1-4172-b7a5-b4633f686d55","resolution":{"observed_at":"2026-08-06T23:10:45.655074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-07-06T18:55:11.576666Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-06T23:10:45.736107Z","title":"The llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:45.736107Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:8ed7502670de1f6bdd2caee4c90133b40fe2d0ac1af3eb990d984499d7878508","observation_id":"d873694a-61ff-4e2e-8518-a284e4869ca4","resolution":{"observed_at":"2026-08-06T23:10:45.736107Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.02463","last_updated":"2023-11-16T12:38:46Z","snapshot_observed_at":"2026-08-08T04:57:05.131540Z","submitted_at":"2023-08-04T17:00:38Z","title":"Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.02463","snapshot_observed_at":"2026-08-06T23:10:45.868971Z","title":"Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data, November 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:45.868971Z"},"links":{"cited_paper":"/paper/2308.02463","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:a50af8dd6da6bd8c54142c0547c72a40a411cb735f6fc89cea2afd0e440753ae","observation_id":"21572ae9-4807-4a72-b881-70ca51cc44b3","resolution":{"observed_at":"2026-08-06T23:10:45.868971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00578","last_updated":"2024-03-31T06:55:12Z","snapshot_observed_at":"2026-07-06T17:53:35.219482Z","submitted_at":"2024-03-31T06:55:12Z","title":"M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00578","snapshot_observed_at":"2026-08-06T23:10:45.988146Z","title":"Meng, and Bo Zhao","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:45.988146Z"},"links":{"cited_paper":"/paper/2404.00578","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:93a321b01f7be671b6835f74d827405326694b11b476ee1718573f295e01f6f2","observation_id":"470dc869-5bc2-4946-ac5e-dde649d80ed8","resolution":{"observed_at":"2026-08-06T23:10:45.988146Z","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":"2026-08-06T23:10:46.089541Z","title":"De- veloping Generalist Foundation Models from a Multi- modal Dataset for 3D Computed Tomography, April 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:46.089541Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:40430c1323e51e443d0ef8bc150b56efc8a4341775be6279dfcf7d1a92c1b6f4","observation_id":"6d3e0d87-338f-4a8a-abce-dad19db84edf","resolution":{"observed_at":"2026-08-06T23:10:46.089541Z","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":"2026-08-06T23:10:47.693950Z","title":"ROUGE: A Package for Automatic Evalu- ation of Summaries","venue":null,"work_id":"0df72351-03c0-4056-8362-88daf6c61cd6","year":2004},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:46.201273Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:394bef3490e69dddbd00811bb2b1b1d9ecaee4179af229fdb99217a8489e1526","observation_id":"89e9e2f8-049d-44d9-a7e0-021817e7a208","resolution":{"observed_at":"2026-08-06T23:10:47.764041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:47.499987Z","title":"Bleu: a Method for Automatic Evaluation of Ma- chine Translation","venue":null,"work_id":"b88aa657-9cd3-49d2-aa25-af332656ead6","year":2002},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:46.283549Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:b503607459675b131b490146c17c3efe3d355000d291e57d7b2d3af6c0fd68f3","observation_id":"49635171-5368-4672-9ab1-54707ea48709","resolution":{"observed_at":"2026-08-06T23:10:47.559786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:10:47.291155Z","title":"METEOR: An Auto- matic Metric for MT Evaluation with Improved Correlation with Human Judgments","venue":null,"work_id":"3b61af58-2d24-418a-a11a-1f296e4a092e","year":2005},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:46.443896Z"},"links":{"citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:e59ff8960c46ebb5b305f13f5fdb70ef674d4dbdd04b8efb1c88f380d84a44e4","observation_id":"cbd352a5-33a0-412f-8bd0-aeffdc2de515","resolution":{"observed_at":"2026-08-06T23:10:47.393684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.09675","last_updated":"2020-02-24T18:59:28Z","snapshot_observed_at":"2026-07-29T15:42:51.774083Z","submitted_at":"2019-04-21T23:08:53Z","title":"BERTScore: Evaluating Text Generation with BERT","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.09675","snapshot_observed_at":"2026-08-06T23:10:46.566102Z","title":"Wein- berger, and Yoav Artzi","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:46.566102Z"},"links":{"cited_paper":"/paper/1904.09675","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:74645b8aabaf956772d87efcf382732d4dfc0a06d679164c64749192399821f3","observation_id":"5fff4125-b51c-4d5c-ba59-8a7cedb4ce4e","resolution":{"observed_at":"2026-08-06T23:10:46.566102Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.03595","last_updated":"2025-01-22T06:24:43Z","snapshot_observed_at":"2026-07-06T18:10:29.627336Z","submitted_at":"2024-05-06T16:04:03Z","title":"GREEN: Generative Radiology Report Evaluation and Error Notation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.03595","snapshot_observed_at":"2026-08-06T23:10:46.701581Z","title":"Chaudhari, and Jean-Benoit Delbrouck","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:46.701581Z"},"links":{"cited_paper":"/paper/2405.03595","citing_paper":"/paper/2506.19217"},"observation_digest":"sha256:1041900a348609ba4d01ee5c58f35278854eb7e7fa1912964ed11b3dd095a2e6","observation_id":"6e5a2bf0-69a9-4a05-8e98-d63b4cb7fd0a","resolution":{"observed_at":"2026-08-06T23:10:46.701581Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.19217","last_updated":"2025-06-24T00:51:03Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T19:22:27.267511Z","submitted_at":"2025-06-24T00:51:03Z","title":"MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":0,"verified_fuzzy":24},"total_outbound_references":40},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.19217."}