{"as_of":"2026-08-14T21:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:78629cb074286c7749c1f0168bd83b0c2e0792c0101050f862382dd3b1fd1841","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T13:00:04.312988Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2507.21302/citation-record","integrity":"/paper/2507.21302/integrity","json":"/paper/2507.21302/citation-record.json","paper":"/paper/2507.21302"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:00:04.914693Z","title":"Ai-driven clinical decision support systems: an ongoing pursuit of potential","venue":null,"work_id":"1c7bfb8b-0ef0-4c02-a4a9-95d0d7361945","year":2024},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.143948Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:d7eb4303aee9230fc28fdf6e7436f2e139480c0a0362365bc1724357e25c88cf","observation_id":"5ab510bd-95c9-4d9c-be81-5c00656768b5","resolution":{"observed_at":"2026-08-06T13:00:04.919167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.12060","last_updated":"2021-12-07T18:56:13Z","snapshot_observed_at":"2026-08-10T10:57:53.089255Z","submitted_at":"2021-02-24T04:25:01Z","title":"Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.12060","snapshot_observed_at":"2026-08-06T13:00:04.148653Z","title":"Teach me to explain: A review of datasets for explainable natural language processing","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.148653Z"},"links":{"cited_paper":"/paper/2102.12060","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:1ca156e4e7c61346255067a4d1f90fa5b2774f1dfd32470526f3acc3e03f20e3","observation_id":"2c7e05a9-89e5-4b07-83b7-9ab22879528f","resolution":{"observed_at":"2026-08-06T13:00:04.148653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04736","last_updated":"2020-10-09T18:00:04Z","snapshot_observed_at":"2026-08-13T21:22:51.328828Z","submitted_at":"2020-10-09T18:00:04Z","title":"Evaluating and Characterizing Human Rationales","version":1},"cited_work":{"arxiv_id":"2010.04736","doi":null,"metadata_source":"pith","pith_arxiv_id":"2010.04736","snapshot_observed_at":"2026-08-06T13:00:04.557096Z","title":"Evaluating and Characterizing Human Rationales","venue":"cs.CL","work_id":"8c866c79-c8ed-4259-a194-2f2b583f437c","year":2020},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.153637Z"},"links":{"cited_paper":"/paper/2010.04736","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:9649943d7fccf7283c992f4a80b336755f50dfa6fce12a3b6da24e8eafffabd6","observation_id":"eaf5b8b6-7bab-4655-878b-642b6e14d7a5","resolution":{"observed_at":"2026-08-06T13:00:04.561756Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.900664Z","title":"Knife: Distilling meta- reasoning knowledge with free-text rationales","venue":null,"work_id":"f70b1491-93a7-4e63-a8bd-2ca2f1dca54a","year":2023},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.158492Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:954452cfa7212038724d6faa4054dc99bbaa71cf92ea223a7f7d92fe75d516e8","observation_id":"2d5e0670-5170-431e-8652-154fdc2233b0","resolution":{"observed_at":"2026-08-06T13:00:04.905516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.07099","last_updated":"2023-11-13T06:13:38Z","snapshot_observed_at":"2026-08-14T11:59:47.757145Z","submitted_at":"2023-11-13T06:13:38Z","title":"Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning","version":1},"cited_work":{"arxiv_id":"2311.07099","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.07099","snapshot_observed_at":"2026-08-06T13:00:04.538013Z","title":"Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning","venue":"cs.CL","work_id":"5cb93e9d-0419-42cc-824a-856560c38304","year":2023},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.163188Z"},"links":{"cited_paper":"/paper/2311.07099","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:be5674b383138214896f6cf3b62f5bea1a1fa00f846c7f4545ecaa7f687c3f7f","observation_id":"d1a9cd3a-ca80-4190-8839-9599945a3e77","resolution":{"observed_at":"2026-08-06T13:00:04.542592Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.887458Z","title":"Large language models are clinical reasoners: Reasoning- aware diagnosis framework with prompt-generated rationales","venue":null,"work_id":"9b944837-a2ca-458c-a424-cc70a1c1a508","year":2024},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.167919Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:4222a24ae79acee489e59ee11b9186d937540ac23e3a1555a95a4d00abce68d8","observation_id":"4b79bb45-4ad2-424e-93e6-87aaad72a61b","resolution":{"observed_at":"2026-08-06T13:00:04.891897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.873184Z","title":"Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine","venue":null,"work_id":"feb92332-5dc5-4280-8355-ec10472b17ce","year":2024},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.172690Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:bb140387f4e57d60e6adea34d28671129f7e773f27ffd7c505679c3fb6a6d941","observation_id":"4382dbe0-193b-4b63-9eda-e6a900e31810","resolution":{"observed_at":"2026-08-06T13:00:04.878686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.859606Z","title":"annotator rationales","venue":null,"work_id":"193e0d32-c575-4b84-bbf2-87bfb1d9ab25","year":2007},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.177539Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:32432db7199eb4e4a6cb25638e387f58b2b359d3849315a8fe0c90e799d72e9e","observation_id":"e7218c9e-aaf5-4d0b-a409-29067a79cf92","resolution":{"observed_at":"2026-08-06T13:00:04.864377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.845955Z","title":"Rationale-augmented convolutional neural networks for text clas- sification","venue":null,"work_id":"a2bd96bf-00df-46bc-a2ca-41456576fbe1","year":2016},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.182413Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:08bd963f7b6f16fb2747274cebd5a5b0540dd02be39690c2e5ddc549d9304cf7","observation_id":"3aa08243-54e6-4dba-ad86-50ad37b2944a","resolution":{"observed_at":"2026-08-06T13:00:04.850321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1808.09367","last_updated":"2018-08-28T15:38:41Z","snapshot_observed_at":"2026-08-14T18:36:07.753608Z","submitted_at":"2018-08-28T15:38:41Z","title":"Deriving Machine Attention from Human Rationales","version":1},"cited_work":{"arxiv_id":"1808.09367","doi":null,"metadata_source":"pith","pith_arxiv_id":"1808.09367","snapshot_observed_at":"2026-08-06T13:00:04.517912Z","title":"Deriving Machine Attention from Human Rationales","venue":"cs.CL","work_id":"73967e97-6d89-4268-a371-edc55e8df64b","year":2018},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.186801Z"},"links":{"cited_paper":"/paper/1808.09367","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:5090cf6a90aad8651e5e5f3bbe53bbd0b31b3d232ee4ee0b9c190a011645e5ed","observation_id":"87872b0b-4df5-4303-9950-0f4e9bac858d","resolution":{"observed_at":"2026-08-06T13:00:04.523516Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.13714","last_updated":"2019-05-31T16:49:57Z","snapshot_observed_at":"2026-08-14T16:22:54.824580Z","submitted_at":"2019-05-31T16:49:57Z","title":"Do Human Rationales Improve Machine Explanations?","version":1},"cited_work":{"arxiv_id":"1905.13714","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.13714","snapshot_observed_at":"2026-08-06T13:00:04.498427Z","title":"Do Human Rationales Improve Machine Explanations?","venue":"cs.CL","work_id":"4dee5a0e-388c-47f4-9d49-c458b5d2f5fc","year":2019},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.191173Z"},"links":{"cited_paper":"/paper/1905.13714","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:0653b358a51b01bc9136ab9bc701d486c836cabac63ce640925420e1764fbc9a","observation_id":"f7b6eee7-9ae4-4345-8474-de28a295db67","resolution":{"observed_at":"2026-08-06T13:00:04.503196Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.06870","last_updated":"2019-08-19T15:11:27Z","snapshot_observed_at":"2026-08-14T12:30:16.976707Z","submitted_at":"2019-08-19T15:11:27Z","title":"Fine-grained Sentiment Analysis with Faithful Attention","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.06870","snapshot_observed_at":"2026-08-06T13:00:04.195671Z","title":"Fine-grained sentiment analysis with faithful attention","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.195671Z"},"links":{"cited_paper":"/paper/1908.06870","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:3a32b101f5942a4381b4a5b75e22c6aafa65a9a1174a4b48b973fbc2f203e92d","observation_id":"e7145913-d836-4cff-b8f2-a92bb6f896e0","resolution":{"observed_at":"2026-08-06T13:00:04.195671Z","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":"2026-08-06T13:00:04.832676Z","title":"Marta: Leveraging human rationales for explainable text classification","venue":null,"work_id":"aaa9d4e4-1e49-4d0f-af1a-e8a5c27e5746","year":2021},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.200828Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:e40b976103a4de09f967e5f2dc06712500c7c07550dc2687dc4d2ef4f5a0bd88","observation_id":"9102abd2-de98-40af-b48f-d35371b8f5f9","resolution":{"observed_at":"2026-08-06T13:00:04.837115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.819796Z","title":"Human-like explanation for text classification with limited attention supervision","venue":null,"work_id":"a07301ce-7602-4444-a70a-fa6d4d658e00","year":2021},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.204846Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:64ac6216a5c54ce831ddbebc41093aaa2e7b30bfedea35eec19883e03ece03dc","observation_id":"f746415d-f886-47ae-828a-3bb9fec51aff","resolution":{"observed_at":"2026-08-06T13:00:04.824105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.806672Z","title":"Learning with rationales for document classification","venue":null,"work_id":"69e62e9e-70bc-4c9b-8d40-fa09adfef714","year":2018},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.208778Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:fd9a9cd6b638222ec708da79353f166f67a4e4c57dc1bea10a766853ea540b42","observation_id":"1d4f5c23-f120-4d64-b688-56d03319fb9e","resolution":{"observed_at":"2026-08-06T13:00:04.810845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.07611","last_updated":"2021-11-15T09:02:10Z","snapshot_observed_at":"2026-08-13T17:34:06.766258Z","submitted_at":"2021-11-15T09:02:10Z","title":"Rationale production to support clinical decision-making","version":1},"cited_work":{"arxiv_id":"2111.07611","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.07611","snapshot_observed_at":"2026-08-06T13:00:04.465913Z","title":"Rationale production to support clinical decision-making","venue":"cs.CL","work_id":"737f7182-712d-4516-8ac7-b76f95940d15","year":2021},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.212687Z"},"links":{"cited_paper":"/paper/2111.07611","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:3870e1ad9fdfa3b1d1467a81cc21392cc71bdddfef566e1c1a16aae565b1334f","observation_id":"0868a2b8-c7c4-4e30-9ac4-77def29b771e","resolution":{"observed_at":"2026-08-06T13:00:04.470556Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.793487Z","title":"Explain and predict, and then predict again","venue":null,"work_id":"f69624b3-cbed-46a0-92b9-ca972f97c95d","year":2021},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.217152Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:cdc9482c2f0d3d9578ef156494b2b0399724acbfdc688da609fa504a3faa940b","observation_id":"53f2f937-d927-452b-8d21-070af4829ff4","resolution":{"observed_at":"2026-08-06T13:00:04.797695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.780332Z","title":"Evaluating explanations: How much do explanations from the teacher aid students? Transactions of the Association for Computational Linguistics, 10:359–375, 2022","venue":null,"work_id":"3f54fafb-c31c-42a7-969d-5289fccf4230","year":2022},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.221202Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:a762f7edb99b9a2950e1a98eb21b3d60ea168980c9afcae0aeda4b6c97092a72","observation_id":"afafb91c-1eda-4efa-85c5-0a205022edaa","resolution":{"observed_at":"2026-08-06T13:00:04.784698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.767331Z","title":"Rationalization for explainable nlp: a survey","venue":null,"work_id":"5d7d68db-905e-419c-b097-1ff966363094","year":2023},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.225179Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:6eac4d91b3915957013914fd6bd12fd206b31137de9dabd65224e3a425b153b0","observation_id":"7b8f7b97-e4b5-4cf1-a4a9-bb60a400cf11","resolution":{"observed_at":"2026-08-06T13:00:04.771527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.03098","last_updated":"2024-04-03T22:39:33Z","snapshot_observed_at":"2026-08-13T00:38:05.920164Z","submitted_at":"2024-04-03T22:39:33Z","title":"Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales","version":1},"cited_work":{"arxiv_id":"2404.03098","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.03098","snapshot_observed_at":"2026-08-06T13:00:04.446274Z","title":"Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales","venue":"cs.CL","work_id":"affdea6d-d5f1-4ba4-8294-7be796d82152","year":2024},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.229165Z"},"links":{"cited_paper":"/paper/2404.03098","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:c1726e364eca2e2ec38a8b685ac0c7c4337a12b76d80c18a6f8f6306f966ca78","observation_id":"9ce6dd69-dc05-4d34-8766-e9d4ad9902fc","resolution":{"observed_at":"2026-08-06T13:00:04.451082Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.01378","last_updated":"2021-09-14T06:51:43Z","snapshot_observed_at":"2026-08-11T21:17:07.807490Z","submitted_at":"2021-03-02T00:36:45Z","title":"Contrastive Explanations for Model Interpretability","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.01378","snapshot_observed_at":"2026-08-06T13:00:04.233455Z","title":"Contrastive explanations for model interpretability","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.233455Z"},"links":{"cited_paper":"/paper/2103.01378","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:8cc1ced127ce571a352a6d37aa989453576b30ac41d9ad2b0cfa14aaee461c95","observation_id":"702ed879-b2c9-44b5-84fb-4fc98fa3e07f","resolution":{"observed_at":"2026-08-06T13:00:04.233455Z","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":"2026-08-06T13:00:04.754720Z","title":"https://seer.cancer.gov/data-software/","venue":null,"work_id":"10d3912c-15c1-4d6f-82a8-b7c4c394dba9","year":2022},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.237768Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:d9328232403205b6b1e97c3d3d423eeac5fca870fb4e6bec7ce66e83c34cc91a","observation_id":"50a6cc3e-bb13-4f6a-b7d1-d50f84c201b3","resolution":{"observed_at":"2026-08-06T13:00:04.758756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.03429","last_updated":"2020-04-24T17:25:40Z","snapshot_observed_at":"2026-08-14T19:29:50.045051Z","submitted_at":"2019-11-08T18:29:03Z","title":"ERASER: A Benchmark to Evaluate Rationalized NLP Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.03429","snapshot_observed_at":"2026-08-06T13:00:04.242009Z","title":"Eraser: A benchmark to evaluate rationalized nlp models","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.242009Z"},"links":{"cited_paper":"/paper/1911.03429","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:1aaf0bed698aff98a6865544d93ba903f89aa6ab0d038663f93a22d74669cbeb","observation_id":"da7f39e7-a5c7-4edf-bc4e-d1c50dedba95","resolution":{"observed_at":"2026-08-06T13:00:04.242009Z","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":"2026-08-06T13:00:04.740521Z","title":"Machine learning and deep learning tools for the automated capture of cancer surveillance data","venue":null,"work_id":"2744280b-c6c0-4af2-ad7b-2dedde3351da","year":2024},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.246384Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:b75502e2968efbe87b02343f27fbd0836f1dff9e07125d9462c19f33f4e05bc2","observation_id":"b3aeeaf0-21df-465f-8482-f939e381c65f","resolution":{"observed_at":"2026-08-06T13:00:04.745164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.727269Z","title":"SEER*DMS User Manual: Chapter 14 - Annotation Tasks, 2020","venue":null,"work_id":"5deba216-49a1-41ba-8cbc-9e21e3b7a228","year":2020},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.250791Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:441fe68451bcb596c2725f55584c6261bb68689db7eab7e0e515e6a20dc2acd1","observation_id":"886f9713-b0d0-48af-8488-34896d57ea0d","resolution":{"observed_at":"2026-08-06T13:00:04.731607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.712705Z","title":"A comparative study of large language model-based zero-shot inference and task-specific supervised classification of breast cancer pathology reports","venue":null,"work_id":"ace2d601-8bba-4272-a3bb-9a1cf29370ba","year":2024},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.254778Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:9c6ccbed39fbae4863369de6592f2940565d93e8f10be0b983f8e7da9cbdfa40","observation_id":"48de48a3-c915-46ba-a8ad-1120514bbc2d","resolution":{"observed_at":"2026-08-06T13:00:04.718086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.11838","last_updated":"2022-04-15T05:46:23Z","snapshot_observed_at":"2026-08-13T16:52:02.236510Z","submitted_at":"2022-01-27T22:51:58Z","title":"Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.11838","snapshot_observed_at":"2026-08-06T13:00:04.259011Z","title":"Clinical-longformer and clinical- bigbird: Transformers for long clinical sequences","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.259011Z"},"links":{"cited_paper":"/paper/2201.11838","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:ce018a93d2293674b433c48dbd027ea92d36bc4df0d9f84e480859315465b6a0","observation_id":"c260ea40-f35a-4f49-b712-efdf6d756d45","resolution":{"observed_at":"2026-08-06T13:00:04.259011Z","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":"2026-08-06T13:00:04.698337Z","title":"Attention mechanisms in clinical text classification: A comparative evaluation","venue":null,"work_id":"4f3ad725-720e-4493-8328-f7cb1b23188b","year":2024},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.264282Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:e7aa829a3f045104df461972e1736147f4cdba7577a8b2d429480f6e6dcf7e50","observation_id":"f98b7f3b-a1d1-416e-b9b2-e492f32293ff","resolution":{"observed_at":"2026-08-06T13:00:04.703666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.684141Z","title":"Deformable phrase level attention: A flexible approach for improving ai based medical coding","venue":null,"work_id":"92f1517a-2616-43e4-94b3-d9ddde9449a7","year":2024},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.268387Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:dc07942fb727700f228d160cf997d3d5c8b5529bee55ca3322c2da7bd5a9ff5a","observation_id":"be1a4063-5032-4790-86af-b67b80e5830c","resolution":{"observed_at":"2026-08-06T13:00:04.689070Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.670734Z","title":"Text classification algorithms: A survey","venue":null,"work_id":"4f4eba48-5979-4de2-a031-389595607f86","year":2019},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.272705Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:6aec380549f07bcf29cf83c4af36960448b15c538da05002960f9dba9584778e","observation_id":"01fd4208-c06f-455f-81bb-afd41d38f1b8","resolution":{"observed_at":"2026-08-06T13:00:04.675031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.656054Z","title":"Limitations of transformers on clinical text classification","venue":null,"work_id":"9a698be7-58dd-4f7d-a4ae-925d8ecb31f1","year":2021},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.277795Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:26f9f9807bd8722e70ca4afbbc478c93ff75bacbe74c9dfdcbf39d286b6f09e4","observation_id":"4213523b-0665-436e-8b71-fc76d6fd658e","resolution":{"observed_at":"2026-08-06T13:00:04.661212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.641278Z","title":"Deep learning–based text classification: a comprehensive review","venue":null,"work_id":"148ddea2-77e9-41bb-974b-d59ace37e0a2","year":2021},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.281852Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:fe7fc62b2fd303654f197d1580ddc6eeb4ede0e8bf11f3e0a560ab021a440854","observation_id":"ab046aa7-9135-4661-85be-21a142100e51","resolution":{"observed_at":"2026-08-06T13:00:04.645978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.627637Z","title":"Automatic classification of cancer pathology reports: a systematic review","venue":null,"work_id":"c6145acd-8fb8-4dae-bf37-ee2e96901935","year":2022},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.286311Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:6c1d59e583031abd265dfe5535a094e4f0cd65040740e3cac383968020f05a89","observation_id":"82b064a1-3678-435d-8bce-a55d270809c7","resolution":{"observed_at":"2026-08-06T13:00:04.632138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.12808","last_updated":"2020-11-11T00:59:17Z","snapshot_observed_at":"2026-08-14T17:59:56.044749Z","submitted_at":"2018-11-13T15:36:42Z","title":"Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.12808","snapshot_observed_at":"2026-08-06T13:00:04.290551Z","title":"Model evaluation, model selection, and algorithm selection in machine learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.290551Z"},"links":{"cited_paper":"/paper/1811.12808","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:7a4394f2ee479b4f231da12d133058d43131a4883ee7fd7810e76f1ced92a68a","observation_id":"87dc8c1e-0a1b-48a1-8a70-454c76f5348c","resolution":{"observed_at":"2026-08-06T13:00:04.290551Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13294","last_updated":"2019-12-15T21:21:15Z","snapshot_observed_at":"2026-07-06T08:33:06.317895Z","submitted_at":"2019-10-29T14:32:54Z","title":"Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control","version":2},"cited_work":{"arxiv_id":"1910.13294","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.13294","snapshot_observed_at":"2026-08-06T13:00:04.370876Z","title":"Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control","venue":"cs.CL","work_id":"5aedc43e-40a9-4c37-9fbe-658d25b51a74","year":2019},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.295974Z"},"links":{"cited_paper":"/paper/1910.13294","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:9965326d8a495a74404396ff6c66f8818d8d46bb1eab4982bb7609f8e30c212c","observation_id":"54005525-e7bf-490b-98d9-cafbbdef795b","resolution":{"observed_at":"2026-08-06T13:00:04.376357Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.612941Z","title":"Is attention explanation? an introduction to the debate","venue":null,"work_id":"a6ade0f6-652c-4482-b334-8cd77f38bdba","year":2022},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.300521Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:368813b808833c6b4d6512b97adafcbae84e39cb878a3c85bdaddafae11d3356","observation_id":"0c0423d7-011c-4624-9968-73385b804bea","resolution":{"observed_at":"2026-08-06T13:00:04.617537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08646","last_updated":"2021-12-28T20:03:28Z","snapshot_observed_at":"2026-08-12T13:25:27.292280Z","submitted_at":"2021-04-17T21:34:10Z","title":"Competency Problems: On Finding and Removing Artifacts in Language Data","version":3},"cited_work":{"arxiv_id":"2104.08646","doi":null,"metadata_source":"pith","pith_arxiv_id":"2104.08646","snapshot_observed_at":"2026-08-06T13:00:04.348687Z","title":"Competency Problems: On Finding and Removing Artifacts in Language Data","venue":"cs.CL","work_id":"6ba390e4-3714-4584-8088-bf42c07dd76c","year":2021},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.304653Z"},"links":{"cited_paper":"/paper/2104.08646","citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:15237064800b59420f08e82498513ee956379474d735385891925b14894b5a22","observation_id":"1a336980-8c5b-4302-afca-1a231fccc133","resolution":{"observed_at":"2026-08-06T13:00:04.355426Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.599396Z","title":"Towards faithful model explanation in nlp: A survey","venue":null,"work_id":"80409beb-62bb-47ce-9eed-3ac12af383a1","year":2024},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.308962Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:0c63aa703794d11d1aedf68fc8b772f813c577dbfb4e79593cf8757975181d9f","observation_id":"7447d046-5b92-423b-9387-297a29defdc6","resolution":{"observed_at":"2026-08-06T13:00:04.603733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T13:00:04.585510Z","title":"What to learn, and how: Toward effective learning from rationales","venue":null,"work_id":"076d15c8-f073-47b4-a4a3-2ffbea0edb5b","year":2022},"citing_paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T13:00:04.312988Z"},"links":{"citing_paper":"/paper/2507.21302"},"observation_digest":"sha256:6c7f124cf5c614986c92362a15a409ff7ba0cb2ab62441b7a24c89e4ea89ab79","observation_id":"e53aec60-6b9c-4360-8ac8-1fcb151c70ed","resolution":{"observed_at":"2026-08-06T13:00:04.590334Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.21302","last_updated":"2025-07-28T19:44:25Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-08T20:48:00.340438Z","submitted_at":"2025-07-28T19:44:25Z","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":8,"verified_fuzzy":24},"total_outbound_references":39},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.21302."}