{"as_of":"2026-08-23T17:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:772939381383e16283644fdc8192fd7a674c8e37e739bc6aa46009e288e17e11","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T19:41:32.860670Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+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/2506.15330/citation-record","integrity":"/paper/2506.15330/integrity","json":"/paper/2506.15330/citation-record.json","paper":"/paper/2506.15330"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:41:33.574943Z","title":"The selection and use of essential in vitro diagnostics: Report of the third meeting of the strategic advisory group of experts on in vitro diagnostics,","venue":null,"work_id":"4cf3211f-6158-4452-8731-e18d5c8d78e4","year":2021},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.713026Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:1882f147e2520896dd1910d9fe2fbae2a7d1acbda2c9f351d254713110da1011","observation_id":"94c72bd2-6d44-40f2-a921-7600d4692dcb","resolution":{"observed_at":"2026-08-15T19:41:33.579702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:32.718013Z","title":"Artificial intelligence in routine blood tests,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.718013Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:97b23f26fb502be0fed3e1d44d1cfaf0b46a2aadcd66f7968db75e2392adf13e","observation_id":"a059f702-7a98-4a54-ac2d-377f8f9bcb75","resolution":{"observed_at":"2026-08-15T19:41:32.718013Z","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":"2026-08-15T19:41:33.561158Z","title":"Usefulness of complete blood count (cbc) to assess cardiovascular and metabolic diseases in clinical settings: A comprehensive literature review,","venue":null,"work_id":"61f68d46-b10f-43f1-93c9-0c1196fab1c2","year":2022},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.722260Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:28bc16d8ef2afa5883520891394d1cdf048abcc6b8226a850f375570ea16cad0","observation_id":"fd9ae321-a74b-4918-a0c9-7d877ff823ec","resolution":{"observed_at":"2026-08-15T19:41:33.565737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1515/cclm-2021-1194","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:41:32.948877Z","title":"Automated prediction of low ferritin concentrations using a machine learning algorithm,","venue":null,"work_id":"8c65c23c-618f-4cc7-9a92-396feef95b4c","year":1921},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.726649Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:e5fe0eb0ab8e637c1a26979edcbb5ff42ac97f42b14788c03f905003d5c778bb","observation_id":"2b2a06a4-6bff-4b9f-9b3a-bb4a26592f20","resolution":{"observed_at":"2026-08-15T19:41:32.953709Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/ajcp/aqw064","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:41:32.934769Z","title":"Using machine learning to predict laboratory test results,","venue":null,"work_id":"9b3f99af-6f4f-4039-8b8a-17416af3b1fc","year":2016},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.731283Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:becb59bda7161af24f1d086bf34f8c89fb51bb45aee2d50d482d9388db512b6c","observation_id":"26fef770-5c13-4684-8d8a-cad82a18c3be","resolution":{"observed_at":"2026-08-15T19:41:32.939258Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.546835Z","title":"Classification and Explanation of Iron Deficiency Anemia from Complete Blood Count Data Using Machine Learning,","venue":null,"work_id":"aa6510ed-cda3-4e6e-a7a8-cc183865590e","year":null},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.735977Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:3b087e2a1750aaee6031413090498c927d163e7fbc14048170c2a10f4ee898f6","observation_id":"d5aca6fc-756c-40d9-a6a1-833be5bf5866","resolution":{"observed_at":"2026-08-15T19:41:33.551355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1155/2022/8114049","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:41:32.920629Z","title":"Use of machine learning and routine laboratory tests for diabetes mellitus screening,","venue":null,"work_id":"82aaf625-4f8d-40f7-803e-344d5d11a319","year":2022},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.744873Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:e5d3e8f533d9f4e8d75ed845587e7b5a00c2be3001cf939ae038fa26c7f9281d","observation_id":"470728f6-aec4-468d-a8de-a836c1617a90","resolution":{"observed_at":"2026-08-15T19:41:32.925316Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.518871Z","title":"A comparative analysis of machine learning models for the detection of undiagnosed diabetes patients,","venue":null,"work_id":"ed3826d4-1663-4d68-91bf-a20d006fc218","year":2024},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.749562Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:7c5f881ebefa228ed30c9fabdd2c3f98a214a53bba35ecff349028c0813ced0b","observation_id":"977486fa-38f5-4c3f-8e9a-8cfedc0c350b","resolution":{"observed_at":"2026-08-15T19:41:33.523116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.13574","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:41:33.195953Z","title":"Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population,","venue":null,"work_id":"213083fe-c8d8-49a7-97a4-ed6416bb8c1e","year":2024},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.753877Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:ae20e9d5cbd2eea7d4c200fce2c61abbc7cbb9d6368a0239f18602acc6354394","observation_id":"779d2e87-245f-4eb5-ac07-898ce5043944","resolution":{"observed_at":"2026-08-15T19:41:33.202795Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.504968Z","title":"Exploration of machine learning for hyperuricemia prediction models based on basic health checkup tests,","venue":null,"work_id":"2377fdb6-4666-4521-b4b9-12f54527c3c1","year":2019},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.758049Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:94384fa662932de51ce69487d71c1b85c198155fd291f3e0653f834390567443","observation_id":"53e77418-c0c1-4ef1-856e-20fbf75a02b9","resolution":{"observed_at":"2026-08-15T19:41:33.509695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1042/bsr20203859","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:41:32.905442Z","title":"Prediction model of random forest for the risk of hyperuricemia in a chinese basic health checkup test,","venue":null,"work_id":"3c8c5a9f-8519-4ec5-b9a6-e7d90cf08546","year":2021},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.762236Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:40aa579108e3c7d623a4395ce9a7afbedc884a252754ca515473fe6e2660b71a","observation_id":"ab141664-ffbd-43c8-9d1f-54170be2cc8c","resolution":{"observed_at":"2026-08-15T19:41:32.910482Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.05112","last_updated":"2024-07-22T12:16:30Z","snapshot_observed_at":"2026-08-16T14:44:47.131608Z","submitted_at":"2023-11-09T02:55:58Z","title":"A Survey of Large Language Models in Medicine: Progress, Application, and Challenge","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.05112","snapshot_observed_at":"2026-08-15T19:41:32.766437Z","title":"A survey of large language models in medicine: Progress, application, and challenge,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.766437Z"},"links":{"cited_paper":"/paper/2311.05112","citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:b9bcef9b63fb7872804cded784005a4fe340373ca2675c647ad95613d80294a2","observation_id":"0700fa43-aeac-4619-95fc-a0f87795fb05","resolution":{"observed_at":"2026-08-15T19:41:32.766437Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.11959","last_updated":"2023-10-26T12:00:03Z","snapshot_observed_at":"2026-08-16T18:15:16.116565Z","submitted_at":"2021-06-22T17:58:10Z","title":"Revisiting Deep Learning Models for Tabular Data","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.11959","snapshot_observed_at":"2026-08-15T19:41:32.771215Z","title":"Revisiting deep learning models for tabular data,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.771215Z"},"links":{"cited_paper":"/paper/2106.11959","citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:7fbcdf7d239c733ee9517e45a63c161fde174a0bb627c23f08d9fab2080e6091","observation_id":"6bd090a0-c906-4321-802b-8d03e72b4583","resolution":{"observed_at":"2026-08-15T19:41:32.771215Z","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":"2026-08-15T19:41:33.491062Z","title":"Blood uric acid prediction with machine learning: Model development and performance comparison,","venue":null,"work_id":"6632a650-dec8-46fc-8509-c36da44da0d5","year":2020},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.775448Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:8c8cccab98f5b9658ce5741ed9b1ce699c22825daafe48d11de8072af63fe0d6","observation_id":"1421ce21-f632-429a-854f-2a45aa4a7f02","resolution":{"observed_at":"2026-08-15T19:41:33.495710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.477959Z","title":"Multi-class classification algorithms for the diagnosis of anemia in an outpatient clinical setting,","venue":null,"work_id":"61d1a299-25fa-4802-8c76-3fa90f3c780a","year":null},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.779381Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:5539b89b07a1901c404e50f0b6eb03c30c6ed61c6e86a82762378a7e14ffa5db","observation_id":"1da9d26b-7320-4984-806a-2657917e3396","resolution":{"observed_at":"2026-08-15T19:41:33.482160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:32.787966Z","title":"A survey on missing data in machine learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.787966Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:f5a849153c9cdb4363dfc769b24090937f1298ec675cdbc41cf399c590a0643b","observation_id":"5b8482aa-d756-4f1d-b111-cea07747d1ac","resolution":{"observed_at":"2026-08-15T19:41:32.787966Z","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":"2026-08-15T19:41:33.455052Z","title":"A review of missing data handling techniques for machine learning,","venue":null,"work_id":"6b3a40e5-d97b-401a-a505-7ffecbc61471","year":2022},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.791877Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:c2a60c539f2dfb331ecb3f000e409d883d1fa15c0c466214a31357b6261410b9","observation_id":"8c70e33d-d1f1-4fd7-991d-8ba413092a18","resolution":{"observed_at":"2026-08-15T19:41:33.459370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.441976Z","title":"A review on genetic algorithm: past, present, and future,","venue":null,"work_id":"22d84735-16ab-4c1b-b405-27e6b2651daa","year":2021},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.795833Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:e577224edc2f86d1e301afa59b88d764696c3bd2c159f0f7749619893e2c58bb","observation_id":"03655a49-32c4-40ba-b0ab-c0b583a144b6","resolution":{"observed_at":"2026-08-15T19:41:33.446164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.427884Z","title":"Development of optimal transmission rate of the kinematic chain by using genetic algorithms coded in mathcad,","venue":null,"work_id":"cdc53858-ee3c-4398-9310-8e70ac012fe6","year":2021},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.800005Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:3c2cb2af091860d070d4fb8821548477a464a9893984e7856bdbcb1d90ccc683","observation_id":"914e05cf-5b32-4b8a-8c38-b087b8085e89","resolution":{"observed_at":"2026-08-15T19:41:33.432622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.413828Z","title":"Gamin: Generative adversarial multiple imputation network for highly missing data,","venue":null,"work_id":"838c97e0-ea26-4fa1-95bc-835fd75ffec0","year":2020},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.804057Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:6464337103bfd7206ef0ad57f51bd315b0eb655071ecb482c6c5ac3994433871","observation_id":"7dcbdec1-7b58-4473-9b6b-49db3a8be4d3","resolution":{"observed_at":"2026-08-15T19:41:33.418579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.399238Z","title":"A survey of missing data imputation using generative adversarial networks,","venue":null,"work_id":"2828852d-acbd-4a96-8de6-4d2a5678de51","year":2020},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.808144Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:0c8016f0f3f580c1e98dd83cbb92c221edd746249461eda76412fd55a1f62a86","observation_id":"32e28e43-3b7f-42a6-9f15-6c44c4144066","resolution":{"observed_at":"2026-08-15T19:41:33.403735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.385029Z","title":"A machine learning model for hemoglobin estimation and anemia classification,","venue":null,"work_id":"7db0dbf1-4c0e-4b8f-a4cb-65d95a959a51","year":2019},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.812389Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:4471332fbea5f3aaaa8616105eb4f6cd41be6b2ec26f923db7daf359f9b82510","observation_id":"0b6394cc-ba2c-40cc-a97e-43f1b321b83a","resolution":{"observed_at":"2026-08-15T19:41:33.389413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.371085Z","title":"Set transformer: A framework for attention-based permutation-invariant neural networks,","venue":null,"work_id":"11312e34-1433-4cff-aae8-07ca5c4e5ff4","year":2019},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.816564Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:889b470505f57e4a2556f0dbff7013b92e52779a31bf80dbac8edc1a6324dc65","observation_id":"fd0022d5-ed4b-404e-b51d-1b4ee004299f","resolution":{"observed_at":"2026-08-15T19:41:33.375709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.355943Z","title":"Attention is All you Need,","venue":null,"work_id":"bed1884c-01e8-4bc1-868b-36776b658587","year":2017},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.820705Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:ea330b7d0cba719304882c0e25618eca37b51108a38ed5a9c4f4778a8e3c61dc","observation_id":"161a9dde-7444-4555-96a3-887adad16eeb","resolution":{"observed_at":"2026-08-15T19:41:33.361473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.341403Z","title":"Available: https://yandex.cloud/en-ru/services/yandexgpt","venue":null,"work_id":"d9b0f086-1d29-42aa-a72f-c5aa78074aee","year":null},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.824822Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:533e926fb4ba820b108fb18414df6345ea46315818e20b5d61afe76c6896d0a4","observation_id":"613d412f-96f8-4331-af61-3c157ccee3a5","resolution":{"observed_at":"2026-08-15T19:41:33.345952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.06678","last_updated":"2020-12-11T23:31:23Z","snapshot_observed_at":"2026-08-14T12:45:55.154777Z","submitted_at":"2020-12-11T23:31:23Z","title":"TabTransformer: Tabular Data Modeling Using Contextual Embeddings","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.06678","snapshot_observed_at":"2026-08-15T19:41:32.829119Z","title":"Tabtransformer: Tabular data modeling using contextual embeddings,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.829119Z"},"links":{"cited_paper":"/paper/2012.06678","citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:6e86b6c85fc22b39e88a2e5a2476a74c8a981369e54af48e6f3f118a27459c59","observation_id":"3c4fc24d-31f3-447b-93a7-bb1f5da9e893","resolution":{"observed_at":"2026-08-15T19:41:32.829119Z","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":"2026-08-15T19:41:32.833826Z","title":"Cholletet al., “Keras,” https://keras.io, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.833826Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:2f0f8cb9e6efe0b31a0e651e3bbe6a31df6f9496f726085fef004b3c61447be1","observation_id":"a1914d0f-5177-4676-84a1-e5f7acc5498a","resolution":{"observed_at":"2026-08-15T19:41:32.833826Z","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":"2026-08-15T19:41:32.838105Z","title":"TensorFlow: Large-scale machine learning on heterogeneous systems,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.838105Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:083554b68f85220906d09938086162b67de131acf4acb1aa0d8685e97c4c964b","observation_id":"358b0533-647c-4679-b6c6-63204aca0833","resolution":{"observed_at":"2026-08-15T19:41:32.838105Z","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":"2026-08-15T19:41:33.309216Z","title":"A survey of multi-task learning methods in chemoinformatics,","venue":null,"work_id":"36008b5c-be4d-4f29-a662-5dd9301ade18","year":null},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.842429Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:6563c822188cfdab8cfc4bd0d8f615a844327666c686c462f94b381c3a7251d7","observation_id":"840739d5-ee15-407c-a7c1-1925ef13aa6b","resolution":{"observed_at":"2026-08-15T19:41:33.313764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05556","last_updated":"2023-10-26T12:11:02Z","snapshot_observed_at":"2026-08-18T08:30:27.326989Z","submitted_at":"2022-03-10T18:59:21Z","title":"On Embeddings for Numerical Features in Tabular Deep Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05556","snapshot_observed_at":"2026-08-15T19:41:32.851496Z","title":"On embeddings for numerical features in tabular deep learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.851496Z"},"links":{"cited_paper":"/paper/2203.05556","citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:bbdfaa8eeaec70169e82c5a66d80eefbd1288442f7114e6a139a5912a363d813","observation_id":"a7ff41e0-c064-4237-b932-a4d824c6f9e0","resolution":{"observed_at":"2026-08-15T19:41:32.851496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.24210","last_updated":"2025-02-18T18:58:14Z","snapshot_observed_at":"2026-08-16T13:04:02.106456Z","submitted_at":"2024-10-31T17:58:41Z","title":"TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.24210","snapshot_observed_at":"2026-08-15T19:41:32.856172Z","title":"Tabm: Advancing tabular deep learning with parameter-efficient ensembling,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.856172Z"},"links":{"cited_paper":"/paper/2410.24210","citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:9668fdd18dca0e439c30c3d540a5975b68205eb9984e55f83ddc3ca1ebd8468f","observation_id":"03d301ba-bf6a-4a88-be4a-7b3d837befb7","resolution":{"observed_at":"2026-08-15T19:41:32.856172Z","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":"2026-08-15T19:41:33.281542Z","title":"Federated learning: Overview, strategies, applications, tools and future directions,","venue":null,"work_id":"515c3fd5-0912-464f-adef-2d578d5bb84e","year":2024},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.860670Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:474d8fc3b85fbbd90afe55f04b398b2242b2fc901f127dd0d8a0e7d4020115dc","observation_id":"a6606cac-cc79-4e46-a1c1-1466670d0fe3","resolution":{"observed_at":"2026-08-15T19:41:33.286069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.295626Z","title":"Available: https://onlinelibrary.wiley.com/doi/abs/10","venue":null,"work_id":"631810dc-e571-4bf1-af56-f67b6a1a0bf0","year":null},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.847216Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:8d847b5f96164bec325d5826c2e6e42228ceadf494460664291e27df3ddfa96f","observation_id":"14b40b1c-4aa2-4423-99de-751c69a31423","resolution":{"observed_at":"2026-08-15T19:41:33.299992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1371/journal.pone.0269685","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:41:32.889833Z","title":"Available: https://doi.org/10.1371/journal.pone.0269685","venue":null,"work_id":"3433d7bb-1b15-46ff-a881-0bd6ec616309","year":null},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.783739Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:34d0044cf4119d3d048d17184c3789c069492d3518355d2909d37adacc43ba01","observation_id":"9f61b930-1f1c-431f-833c-c69c98d7c940","resolution":{"observed_at":"2026-08-15T19:41:32.895412Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-15T19:41:33.532024Z","title":"Available: https://www.mdpi.com/2673-7426/4/1/36","venue":null,"work_id":"57379ee6-b17f-4ad4-b648-80f6c19bdbfa","year":null},"citing_paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-15T19:41:32.740461Z"},"links":{"citing_paper":"/paper/2506.15330"},"observation_digest":"sha256:e3ec2da6e20dfc6d9431a63f7059beb85b95393025cb5eb5d86d223e769768d8","observation_id":"38869c8c-6e1a-41fb-ab90-8c7e877067b3","resolution":{"observed_at":"2026-08-15T19:41:33.536242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.15330","last_updated":"2025-06-18T10:10:02Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-20T01:58:33.089342Z","submitted_at":"2025-06-18T10:10:02Z","title":"Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":9,"verified_exact":5,"verified_fuzzy":20},"total_outbound_references":35},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2506.15330."}