{"as_of":"2026-08-19T05:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4146060d24fc1c280a8b38f8c70099eb935c003acc35847de393cb19e137bfb0","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T19:55:57.490647Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2506.14400/citation-record","integrity":"/paper/2506.14400/integrity","json":"/paper/2506.14400/citation-record.json","paper":"/paper/2506.14400"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:55:57.389512Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.389512Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:6bc00adde2e87304f7b1487da9cf64532fc36b83fdc377548b093ae5c22909f3","observation_id":"ff469cd8-daf1-4d8d-9386-f00459fb6ca0","resolution":{"observed_at":"2026-08-15T19:55:57.389512Z","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":"2026-08-15T19:55:57.395014Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.395014Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:22d39f6368392b21e87810047250072bb54095050f7b538c7e06f14249b86e90","observation_id":"18e37727-ead9-4837-a25f-bc3e857624c2","resolution":{"observed_at":"2026-08-15T19:55:57.395014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14861","last_updated":"2026-05-06T07:17:56Z","snapshot_observed_at":"2026-08-10T00:50:00.290036Z","submitted_at":"2024-07-20T12:42:24Z","title":"Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes","version":3},"cited_work":{"arxiv_id":"2407.14861","doi":"10.48550/arxiv.2407.14861","metadata_source":"pith","pith_arxiv_id":"2407.14861","snapshot_observed_at":"2026-08-16T12:16:17.039197Z","title":"Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes","venue":"stat.ML","work_id":"c4bdb8cc-ddbd-4a7f-9778-a1e0182475c4","year":2024},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.400085Z"},"links":{"cited_paper":"/paper/2407.14861","citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:2ed06b04b6cbee59c09fec078d07b23e9118ea6b3f93f4a87641be3488f2ccf8","observation_id":"626ede50-042d-47fc-94b1-3df06d0019eb","resolution":{"observed_at":"2026-08-15T19:55:57.693212Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T19:55:57.406141Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.406141Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:26eb29690841fbdf34e9f377a715131b1af1b645f8606a97aace829e1ab73ccc","observation_id":"7bbd5cb9-5de8-47b2-8c71-100c81b3f4e4","resolution":{"observed_at":"2026-08-15T19:55:57.406141Z","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":"2026-08-15T19:55:58.135237Z","title":null,"venue":null,"work_id":"9042086f-f965-4841-8ae6-39e78d6e95f8","year":2024},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.411605Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:9e3cbc5cb93213b43283042f798bb252e47e95297f55fcf2e1c83d0be0de7dce","observation_id":"e67c3a8f-7371-4ce7-98f6-553458a38f26","resolution":{"observed_at":"2026-08-15T19:55:58.140217Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.08362","last_updated":"2019-09-10T04:12:17Z","snapshot_observed_at":"2026-08-14T18:49:02.743168Z","submitted_at":"2018-07-22T20:37:45Z","title":"An Intersectional Definition of Fairness","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.08362","snapshot_observed_at":"2026-08-15T19:55:57.416317Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.416317Z"},"links":{"cited_paper":"/paper/1807.08362","citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:007e9a57b8b7ef5f11740ef16889b8e5d60bbd6a13ee5fb411f4e831f9cad333","observation_id":"bc258f4b-9438-4bb2-81dc-cb44c6dcdfd3","resolution":{"observed_at":"2026-08-15T19:55:57.416317Z","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":"2026-08-15T19:55:58.119289Z","title":null,"venue":null,"work_id":"014bbfbc-07e2-4b6e-a42a-17b660937c52","year":2024},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.421331Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:d89659bd738e0d88b6bc9dae046ab1066558fda6236d20ed5dfe7135badfcae6","observation_id":"7362aeca-051f-4c72-a6df-c35f525ec1fa","resolution":{"observed_at":"2026-08-15T19:55:58.124339Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T19:55:57.425880Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.425880Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:28ea31955dafc8bc7a7dce0088a5dc2a102927b39e0d3212e065be1664e247ef","observation_id":"290ac3b7-482f-4d96-a10e-3de0e8f59000","resolution":{"observed_at":"2026-08-15T19:55:57.425880Z","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":"10.1016/s2589-7500(24)00112-2","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:55:57.641232Z","title":null,"venue":null,"work_id":"40566f47-cf8f-4b91-a408-e020422c6c99","year":2024},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.431008Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:b8a1048b84007395f681c42e0dfd43c1aeb873ff6f1fb58363edceb2227e851c","observation_id":"eb5948ac-11f4-4cbd-ac73-ac7418738008","resolution":{"observed_at":"2026-08-15T19:55:57.645919Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T19:55:57.435473Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.435473Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:48da8d65c19455357cf17d3da727681f917866a284e5a079476215089b56e6e5","observation_id":"964a665c-7818-49df-9bda-abc9756f33bc","resolution":{"observed_at":"2026-08-15T19:55:57.435473Z","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":"2026-08-15T19:55:57.440008Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.440008Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:4bf5b8fb35c4617fa689b14576235030039923eebdf33d0e65ac688bcf584f37","observation_id":"eae710b4-3a23-4a8b-85b5-5fffca44311e","resolution":{"observed_at":"2026-08-15T19:55:57.440008Z","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":"2022.90296","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:55:58.040256Z","title":null,"venue":null,"work_id":"140fee3b-620b-4a6b-9731-4c0180169021","year":2022},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.444524Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:498e38a2bed5bc8cebd87c9cc4765ff873828584b10fbd2590c268edf05b36c9","observation_id":"878eae49-eb11-4ba8-849c-c8864077de5e","resolution":{"observed_at":"2026-08-15T19:55:58.047512Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2024.bionlp-1.41","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:55:57.605895Z","title":null,"venue":null,"work_id":"c335f96b-ff24-451a-9671-2927789b79d0","year":2024},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.449417Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:612255e9c28391f7cb8e1ef13286adabf308ebae6109ec4fcd00525fbf57fe44","observation_id":"39c324de-349e-4d90-b70b-ccd62cdf149f","resolution":{"observed_at":"2026-08-15T19:55:57.610690Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.01497","last_updated":"2024-12-06T08:53:43Z","snapshot_observed_at":"2026-08-16T13:21:57.268780Z","submitted_at":"2024-09-02T23:37:20Z","title":"DiversityMedQA: Assessing Demographic Biases in Medical Diagnosis using Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.01497","snapshot_observed_at":"2026-08-15T19:55:57.453939Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.453939Z"},"links":{"cited_paper":"/paper/2409.01497","citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:e7d919c548537a51cccd93abcf9c43b1fc8b97c8e5f73c2283fc67be4f929e1c","observation_id":"fef45328-b69d-472d-9a52-5f040fc4c02a","resolution":{"observed_at":"2026-08-15T19:55:57.453939Z","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":"2022.97944","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:55:57.958097Z","title":null,"venue":null,"work_id":"0fa5b4ab-9bcb-4511-a7bd-fecfd575e36d","year":2022},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.459090Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:1f5c7470290cc6dfa7dc52f081678a5c4e6a20578eb00af80bfadf98d4f8101f","observation_id":"e542b1fd-c0d4-41fd-8a06-d8a16ec89cb4","resolution":{"observed_at":"2026-08-15T19:55:57.968152Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-662-67008-8","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-17T11:03:46.409680Z","title":null,"venue":"Essentials","work_id":"798d5116-50b4-4488-b789-cd1d1a355c10","year":2023},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.463361Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:f53f9cdbd6577191c6ed05a0e9f10fc68cf7d3cb27cabe23e6c66b0a66cd063a","observation_id":"005c754f-3b08-43a0-9f1f-6eff3699d324","resolution":{"observed_at":"2026-08-15T19:55:57.579489Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1142/9789811232701_0022","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:55:57.560816Z","title":null,"venue":null,"work_id":"4f693da8-5876-4bac-9095-ecad2d219985","year":2020},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.467941Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:f5c292621755c7a0d722ca610e08f0742142aed9f75898e20ffa2e6383c93d40","observation_id":"ba92ae67-c119-4f0b-95bf-885870746e84","resolution":{"observed_at":"2026-08-15T19:55:57.565335Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T19:55:57.472405Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.472405Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:42826419eb0802a8fea56944033288424242534b65758405e0eea0ec3fc257bd","observation_id":"ca1a2493-b3f2-4dc7-888f-5b8a4c4b22dd","resolution":{"observed_at":"2026-08-15T19:55:57.472405Z","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":"2026-08-15T19:55:57.476719Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.476719Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:774c3b3758f99832cf7188bc71cb716e69dadd90c87d7c3a4579d7716da97df9","observation_id":"c0b29e78-f1ee-4bfc-b438-c7cbc01f6734","resolution":{"observed_at":"2026-08-15T19:55:57.476719Z","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":"2026-08-15T19:55:57.481306Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.481306Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:d742ea27b57c453091324ac8a0c69fc3ae551958d87f9b819c619d441b3ec70f","observation_id":"1257b394-a701-4ce2-8279-2d5d9ac8baf2","resolution":{"observed_at":"2026-08-15T19:55:57.481306Z","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":"10.18653/v1/2021.emnlp-main.329","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:55:57.536018Z","title":null,"venue":null,"work_id":"239bfff1-e413-471d-8996-f6ed74f68fc2","year":2021},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.485905Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:56a38386fd93a48cfbcee97554b8225f052ae4adf8234e8ceb0b82c6de8ba867","observation_id":"5762c47a-d4b1-41a2-b2ea-030ba2195bef","resolution":{"observed_at":"2026-08-15T19:55:57.540778Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2022.findings-emnlp.144","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:55:57.520003Z","title":null,"venue":null,"work_id":"4ed522b8-9d46-42aa-9571-afab5f0c664a","year":2022},"citing_paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T19:55:57.490647Z"},"links":{"citing_paper":"/paper/2506.14400"},"observation_digest":"sha256:3b3a58fcf8d1e5721f9ad2eec8ca761f40a8c623b3bda1f1aec47bce6c97335e","observation_id":"2d0292e4-72dc-432d-b528-d634648bacd2","resolution":{"observed_at":"2026-08-15T19:55:57.525639Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.14400","last_updated":"2025-06-17T10:59:02Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T19:50:30.179958Z","submitted_at":"2025-06-17T10:59:02Z","title":"One Size Fits None: Rethinking Fairness in Medical AI"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":13,"verified_exact":7,"verified_fuzzy":0},"total_outbound_references":22},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2506.14400."}