{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MBWHHNU4KNWEZFDYTWMQPCWDAZ","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":"f8232d3fe80d922255ee3e9559af52f60ae5fdc9fad9c282e488e4fce54d96fa","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-25T19:49:30Z","title_canon_sha256":"fe8dc9b441f6598cf937d74b2361fde032bb1de4ee8c8495da691d6c9cabf295"},"schema_version":"1.0","source":{"id":"2504.18671","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.18671","created_at":"2026-07-05T10:54:18Z"},{"alias_kind":"arxiv_version","alias_value":"2504.18671v1","created_at":"2026-07-05T10:54:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.18671","created_at":"2026-07-05T10:54:18Z"},{"alias_kind":"pith_short_12","alias_value":"MBWHHNU4KNWE","created_at":"2026-07-05T10:54:18Z"},{"alias_kind":"pith_short_16","alias_value":"MBWHHNU4KNWEZFDY","created_at":"2026-07-05T10:54:18Z"},{"alias_kind":"pith_short_8","alias_value":"MBWHHNU4","created_at":"2026-07-05T10:54:18Z"}],"graph_snapshots":[{"event_id":"sha256:7f955ac1f6b3c0a5a6918966ff8a1cce02abae66c546a86820aa6a39005cd61f","target":"graph","created_at":"2026-07-05T10:54:18Z","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/2504.18671/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mild Traumatic Brain Injury (TBI) detection presents significant challenges due to the subtle and often ambiguous presentation of symptoms in medical imaging, making accurate diagnosis a complex task. To address these challenges, we propose Proof-of-TBI, a medical diagnosis support system that integrates multiple fine-tuned vision-language models with the OpenAI-o3 reasoning large language model (LLM). Our approach fine-tunes multiple vision-language models using a labeled dataset of TBI MRI scans, training them to diagnose TBI symptoms effectively. The predictions from these models are aggreg","authors_text":"Alberto E. Musto, Ambrosio Valencia-Romero, Atmaram Yarlagadda, Christopher Rhea, Donna Broshek, Donna Edmonds, Eranga Bandara, Heather Richter, Lobat Tayebi, Pratip Rana, Ross Gore, Sachin Shetty, Steven Wallace","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-25T19:49:30Z","title":"Proof-of-TBI -- Fine-Tuned Vision Language Model Consortium and OpenAI-o3 Reasoning LLM-Based Medical Diagnosis Support System for Mild Traumatic Brain Injury (TBI) Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.18671","kind":"arxiv","version":1},"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:ec66f1a983817124d38ce1385cce9e9c0e1f058bb8b19012b47cb522629cb9a1","target":"record","created_at":"2026-07-05T10:54:18Z","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":"f8232d3fe80d922255ee3e9559af52f60ae5fdc9fad9c282e488e4fce54d96fa","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-25T19:49:30Z","title_canon_sha256":"fe8dc9b441f6598cf937d74b2361fde032bb1de4ee8c8495da691d6c9cabf295"},"schema_version":"1.0","source":{"id":"2504.18671","kind":"arxiv","version":1}},"canonical_sha256":"606c73b69c536c4c94789d99078ac3066c8c09303dd67d9b03feabec16f577d7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"606c73b69c536c4c94789d99078ac3066c8c09303dd67d9b03feabec16f577d7","first_computed_at":"2026-07-05T10:54:18.738401Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:54:18.738401Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ywSzSRGZuoU1RWLIXptZxy7gInDMULF2pQ98FJjRPBQUXM83bSwbz32tL2arCuAk0xSukFpNF1W23/O4MFKTBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:54:18.738975Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.18671","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ec66f1a983817124d38ce1385cce9e9c0e1f058bb8b19012b47cb522629cb9a1","sha256:7f955ac1f6b3c0a5a6918966ff8a1cce02abae66c546a86820aa6a39005cd61f"],"state_sha256":"270ec4a7737975b8f22a4f2e84214cccfde45d47db5f0f706cd52c9ed1c5c41b"}