{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:VBHWDDGWUGXFUCXKO5OB4QFKTM","short_pith_number":"pith:VBHWDDGW","canonical_record":{"source":{"id":"2503.13281","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-17T15:31:55Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"1dab63162bc9fef521d87590435475c118f3694483beec7e752671af95893ea4","abstract_canon_sha256":"9093fd6fba4b52bb1ffbcf8b8d73746130af005aff1824bc32659e4d38f36dce"},"schema_version":"1.0"},"canonical_sha256":"a84f618cd6a1ae5a0aea775c1e40aa9b0c6a863f6f304440cdac472bf413d92d","source":{"kind":"arxiv","id":"2503.13281","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.13281","created_at":"2026-07-05T10:38:44Z"},{"alias_kind":"arxiv_version","alias_value":"2503.13281v3","created_at":"2026-07-05T10:38:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.13281","created_at":"2026-07-05T10:38:44Z"},{"alias_kind":"pith_short_12","alias_value":"VBHWDDGWUGXF","created_at":"2026-07-05T10:38:44Z"},{"alias_kind":"pith_short_16","alias_value":"VBHWDDGWUGXFUCXK","created_at":"2026-07-05T10:38:44Z"},{"alias_kind":"pith_short_8","alias_value":"VBHWDDGW","created_at":"2026-07-05T10:38:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:VBHWDDGWUGXFUCXKO5OB4QFKTM","target":"record","payload":{"canonical_record":{"source":{"id":"2503.13281","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-17T15:31:55Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"1dab63162bc9fef521d87590435475c118f3694483beec7e752671af95893ea4","abstract_canon_sha256":"9093fd6fba4b52bb1ffbcf8b8d73746130af005aff1824bc32659e4d38f36dce"},"schema_version":"1.0"},"canonical_sha256":"a84f618cd6a1ae5a0aea775c1e40aa9b0c6a863f6f304440cdac472bf413d92d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:38:44.578175Z","signature_b64":"mdzrrXSI2+Ym26CCk2KrXLrHFxCbDsERnYHFWpUiEsX/KbqmmY0VAD7QYiyPWp2LFnnUBjlyuXjqrPZHqjoBDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a84f618cd6a1ae5a0aea775c1e40aa9b0c6a863f6f304440cdac472bf413d92d","last_reissued_at":"2026-07-05T10:38:44.577608Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:38:44.577608Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.13281","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:38:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4Pr1KH+zswdiTwtOmKuZPXAemb8r7p/l2KhR4A7Kw2iMrmQYEebF2VzBKkdM3FbAOXsLN9HCxLdC5yYtQbxkAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:52:40.785181Z"},"content_sha256":"9f20aebb3d1b43b50eabf5a10b1a7ddb3d17ece9786ec6001c4f509ec6460451","schema_version":"1.0","event_id":"sha256:9f20aebb3d1b43b50eabf5a10b1a7ddb3d17ece9786ec6001c4f509ec6460451"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:VBHWDDGWUGXFUCXKO5OB4QFKTM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LLM-Match: An Open-Sourced Patient Matching Model Based on Large Language Models and Retrieval-Augmented Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Chung Il Wi, Cui Tao, James R Cerhan, Maria Vassilaki, Nansu Zong, Owen Garrick, Shaika Chowdhury, Terence T Sio, Xiaodi Li, Xiaoke Liu, Young J Juhn","submitted_at":"2025-03-17T15:31:55Z","abstract_excerpt":"Patient matching is the process of linking patients to appropriate clinical trials by accurately identifying and matching their medical records with trial eligibility criteria. We propose LLM-Match, a novel framework for patient matching leveraging fine-tuned open-source large language models. Our approach consists of four key components. First, a retrieval-augmented generation (RAG) module extracts relevant patient context from a vast pool of electronic health records (EHRs). Second, a prompt generation module constructs input prompts by integrating trial eligibility criteria (both inclusion "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.13281","kind":"arxiv","version":3},"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/2503.13281/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:38:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zgPKVst4zv1FJXq8UP+d4oK5QwRth1WTWJYVSH6c60Z/9iCixAHq5Nx4m2o9zQegHvXnv8I97gCtJLJo/6AdDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:52:40.785724Z"},"content_sha256":"d40a89b95717e976f162e02e217feb4eb56ee6b7c11218ae8b4c93cd639f8f3c","schema_version":"1.0","event_id":"sha256:d40a89b95717e976f162e02e217feb4eb56ee6b7c11218ae8b4c93cd639f8f3c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VBHWDDGWUGXFUCXKO5OB4QFKTM/bundle.json","state_url":"https://pith.science/pith/VBHWDDGWUGXFUCXKO5OB4QFKTM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VBHWDDGWUGXFUCXKO5OB4QFKTM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T04:52:40Z","links":{"resolver":"https://pith.science/pith/VBHWDDGWUGXFUCXKO5OB4QFKTM","bundle":"https://pith.science/pith/VBHWDDGWUGXFUCXKO5OB4QFKTM/bundle.json","state":"https://pith.science/pith/VBHWDDGWUGXFUCXKO5OB4QFKTM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VBHWDDGWUGXFUCXKO5OB4QFKTM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:VBHWDDGWUGXFUCXKO5OB4QFKTM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9093fd6fba4b52bb1ffbcf8b8d73746130af005aff1824bc32659e4d38f36dce","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-17T15:31:55Z","title_canon_sha256":"1dab63162bc9fef521d87590435475c118f3694483beec7e752671af95893ea4"},"schema_version":"1.0","source":{"id":"2503.13281","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.13281","created_at":"2026-07-05T10:38:44Z"},{"alias_kind":"arxiv_version","alias_value":"2503.13281v3","created_at":"2026-07-05T10:38:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.13281","created_at":"2026-07-05T10:38:44Z"},{"alias_kind":"pith_short_12","alias_value":"VBHWDDGWUGXF","created_at":"2026-07-05T10:38:44Z"},{"alias_kind":"pith_short_16","alias_value":"VBHWDDGWUGXFUCXK","created_at":"2026-07-05T10:38:44Z"},{"alias_kind":"pith_short_8","alias_value":"VBHWDDGW","created_at":"2026-07-05T10:38:44Z"}],"graph_snapshots":[{"event_id":"sha256:d40a89b95717e976f162e02e217feb4eb56ee6b7c11218ae8b4c93cd639f8f3c","target":"graph","created_at":"2026-07-05T10:38:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.13281/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Patient matching is the process of linking patients to appropriate clinical trials by accurately identifying and matching their medical records with trial eligibility criteria. We propose LLM-Match, a novel framework for patient matching leveraging fine-tuned open-source large language models. Our approach consists of four key components. First, a retrieval-augmented generation (RAG) module extracts relevant patient context from a vast pool of electronic health records (EHRs). Second, a prompt generation module constructs input prompts by integrating trial eligibility criteria (both inclusion ","authors_text":"Chung Il Wi, Cui Tao, James R Cerhan, Maria Vassilaki, Nansu Zong, Owen Garrick, Shaika Chowdhury, Terence T Sio, Xiaodi Li, Xiaoke Liu, Young J Juhn","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-17T15:31:55Z","title":"LLM-Match: An Open-Sourced Patient Matching Model Based on Large Language Models and Retrieval-Augmented Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.13281","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9f20aebb3d1b43b50eabf5a10b1a7ddb3d17ece9786ec6001c4f509ec6460451","target":"record","created_at":"2026-07-05T10:38:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9093fd6fba4b52bb1ffbcf8b8d73746130af005aff1824bc32659e4d38f36dce","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-17T15:31:55Z","title_canon_sha256":"1dab63162bc9fef521d87590435475c118f3694483beec7e752671af95893ea4"},"schema_version":"1.0","source":{"id":"2503.13281","kind":"arxiv","version":3}},"canonical_sha256":"a84f618cd6a1ae5a0aea775c1e40aa9b0c6a863f6f304440cdac472bf413d92d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a84f618cd6a1ae5a0aea775c1e40aa9b0c6a863f6f304440cdac472bf413d92d","first_computed_at":"2026-07-05T10:38:44.577608Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:38:44.577608Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mdzrrXSI2+Ym26CCk2KrXLrHFxCbDsERnYHFWpUiEsX/KbqmmY0VAD7QYiyPWp2LFnnUBjlyuXjqrPZHqjoBDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:38:44.578175Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.13281","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9f20aebb3d1b43b50eabf5a10b1a7ddb3d17ece9786ec6001c4f509ec6460451","sha256:d40a89b95717e976f162e02e217feb4eb56ee6b7c11218ae8b4c93cd639f8f3c"],"state_sha256":"6aaf21b0f3a2b96c6085a537dcbc81c72605f51f398822d4e05200227d520178"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cBpZi8/TyQclRSJLMVAzzslZXUHmlk/TAu4c4jyqQXkSaYL8uYIJnlpilo+VzLMUB9d2EjYXan674wmz6FvIBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T04:52:40.794517Z","bundle_sha256":"114eeeae3094fd1183d675946a4df093c5c4debb005973aafba3280387b1f34b"}}