{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HI4BJEVCSLG5KQB2TLNOUEWSDL","short_pith_number":"pith:HI4BJEVC","schema_version":"1.0","canonical_sha256":"3a381492a292cdd5403a9adaea12d21af47151165405fd464e4ceadf06191af3","source":{"kind":"arxiv","id":"2502.01691","version":1},"attestation_state":"computed","paper":{"title":"Agent-Based Uncertainty Awareness Improves Automated Radiology Report Labeling with an Open-Source Large Language Model","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dan Turner, Gili Focht, Hadas Ben-Atya, Moti Freiman, Naama Gavrielov, Ruth Cytter-Kuint, Talar Hagopian, Zvi Badash","submitted_at":"2025-02-02T16:57:03Z","abstract_excerpt":"Reliable extraction of structured data from radiology reports using Large Language Models (LLMs) remains challenging, especially for complex, non-English texts like Hebrew. This study introduces an agent-based uncertainty-aware approach to improve the trustworthiness of LLM predictions in medical applications. We analyzed 9,683 Hebrew radiology reports from Crohn's disease patients (from 2010 to 2023) across three medical centers. A subset of 512 reports was manually annotated for six gastrointestinal organs and 15 pathological findings, while the remaining reports were automatically annotated"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2502.01691","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-02T16:57:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"99b159760b78dcd9a17398c8d54e23da0122f553c900742b09a63e713ba885c4","abstract_canon_sha256":"474f6dd55717eea255a5f5271286b3acc3e317b224b22802b90141b40cea897e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:09:17.839604Z","signature_b64":"IJyq9R5Iy5VaVzJ7guHkzpW8lvc6MJMLeTQ+hARwVccThCgIbYPfTCnyvsJ8rxzRYlprPHOgG4/RQHPpUTL1Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a381492a292cdd5403a9adaea12d21af47151165405fd464e4ceadf06191af3","last_reissued_at":"2026-07-05T10:09:17.839080Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:09:17.839080Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Agent-Based Uncertainty Awareness Improves Automated Radiology Report Labeling with an Open-Source Large Language Model","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dan Turner, Gili Focht, Hadas Ben-Atya, Moti Freiman, Naama Gavrielov, Ruth Cytter-Kuint, Talar Hagopian, Zvi Badash","submitted_at":"2025-02-02T16:57:03Z","abstract_excerpt":"Reliable extraction of structured data from radiology reports using Large Language Models (LLMs) remains challenging, especially for complex, non-English texts like Hebrew. This study introduces an agent-based uncertainty-aware approach to improve the trustworthiness of LLM predictions in medical applications. We analyzed 9,683 Hebrew radiology reports from Crohn's disease patients (from 2010 to 2023) across three medical centers. A subset of 512 reports was manually annotated for six gastrointestinal organs and 15 pathological findings, while the remaining reports were automatically annotated"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01691","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.01691/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.01691","created_at":"2026-07-05T10:09:17.839142+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.01691v1","created_at":"2026-07-05T10:09:17.839142+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01691","created_at":"2026-07-05T10:09:17.839142+00:00"},{"alias_kind":"pith_short_12","alias_value":"HI4BJEVCSLG5","created_at":"2026-07-05T10:09:17.839142+00:00"},{"alias_kind":"pith_short_16","alias_value":"HI4BJEVCSLG5KQB2","created_at":"2026-07-05T10:09:17.839142+00:00"},{"alias_kind":"pith_short_8","alias_value":"HI4BJEVC","created_at":"2026-07-05T10:09:17.839142+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HI4BJEVCSLG5KQB2TLNOUEWSDL","json":"https://pith.science/pith/HI4BJEVCSLG5KQB2TLNOUEWSDL.json","graph_json":"https://pith.science/api/pith-number/HI4BJEVCSLG5KQB2TLNOUEWSDL/graph.json","events_json":"https://pith.science/api/pith-number/HI4BJEVCSLG5KQB2TLNOUEWSDL/events.json","paper":"https://pith.science/paper/HI4BJEVC"},"agent_actions":{"view_html":"https://pith.science/pith/HI4BJEVCSLG5KQB2TLNOUEWSDL","download_json":"https://pith.science/pith/HI4BJEVCSLG5KQB2TLNOUEWSDL.json","view_paper":"https://pith.science/paper/HI4BJEVC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.01691&json=true","fetch_graph":"https://pith.science/api/pith-number/HI4BJEVCSLG5KQB2TLNOUEWSDL/graph.json","fetch_events":"https://pith.science/api/pith-number/HI4BJEVCSLG5KQB2TLNOUEWSDL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HI4BJEVCSLG5KQB2TLNOUEWSDL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HI4BJEVCSLG5KQB2TLNOUEWSDL/action/storage_attestation","attest_author":"https://pith.science/pith/HI4BJEVCSLG5KQB2TLNOUEWSDL/action/author_attestation","sign_citation":"https://pith.science/pith/HI4BJEVCSLG5KQB2TLNOUEWSDL/action/citation_signature","submit_replication":"https://pith.science/pith/HI4BJEVCSLG5KQB2TLNOUEWSDL/action/replication_record"}},"created_at":"2026-07-05T10:09:17.839142+00:00","updated_at":"2026-07-05T10:09:17.839142+00:00"}