{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:4FJLRCMJURPF73GTJBQX24OTH5","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":"efbce5f3c4218b470712d8f6f9ee6837fe255d9d0a7c069bc7a4ccc66aaa591b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-16T22:33:14Z","title_canon_sha256":"594f41d5a60b647af12bae007e1c5303f51ea44c95e213e9310da65e46666f91"},"schema_version":"1.0","source":{"id":"2606.20723","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.20723","created_at":"2026-06-23T00:11:54Z"},{"alias_kind":"arxiv_version","alias_value":"2606.20723v1","created_at":"2026-06-23T00:11:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.20723","created_at":"2026-06-23T00:11:54Z"},{"alias_kind":"pith_short_12","alias_value":"4FJLRCMJURPF","created_at":"2026-06-23T00:11:54Z"},{"alias_kind":"pith_short_16","alias_value":"4FJLRCMJURPF73GT","created_at":"2026-06-23T00:11:54Z"},{"alias_kind":"pith_short_8","alias_value":"4FJLRCMJ","created_at":"2026-06-23T00:11:54Z"}],"graph_snapshots":[{"event_id":"sha256:6db43e71ab271d7c7e97e05ce1bbfa0b88856145f8fbd4b58e472e31c960e93f","target":"graph","created_at":"2026-06-23T00:11:54Z","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/2606.20723/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Chronic wound assessment remains a clinically challenging task that requires accurate interpretation of wound morphology, tissue composition, vascular characteristics, and infection risk. Recent advances in Vision-Language Models (VLMs) have introduced the possibility of automated multimodal wound analysis through image understanding combined with clinical reasoning. This study evaluates the performance of several general-purpose and medically specialized open-source and proprietary VLMs for clinical wound assessment using an expanded, curated dataset of 20 clinically diverse wounds spanning v","authors_text":"Justin W. Ady, Mohammed Saim Ahmed Quadri, Neal Panse, Usman Roshan, Yunzhe Xue","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-16T22:33:14Z","title":"Evaluation of Medical Vision Language Models HuluMed and MedGemma, and general purpose chatbots Gemma 3, ChatGPT Plus, and Claude Pro on real previously unseen wound images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.20723","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":"sha256:0cb646d21973aceb59a3153ca4679ee52f2d71e9ba66cb697bc87f3e921b5ddb","target":"record","created_at":"2026-06-23T00:11:54Z","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":"efbce5f3c4218b470712d8f6f9ee6837fe255d9d0a7c069bc7a4ccc66aaa591b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-16T22:33:14Z","title_canon_sha256":"594f41d5a60b647af12bae007e1c5303f51ea44c95e213e9310da65e46666f91"},"schema_version":"1.0","source":{"id":"2606.20723","kind":"arxiv","version":1}},"canonical_sha256":"e152b88989a45e5fecd348617d71d33f48c5f058c6d6754ba435afba7a4961f7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e152b88989a45e5fecd348617d71d33f48c5f058c6d6754ba435afba7a4961f7","first_computed_at":"2026-06-23T00:11:54.723301Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-23T00:11:54.723301Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rLkpLvBsF7+etWUKF+k7Is5y0e7G3vxeL9WHyLcIkQRi08jDXuo4JCP74nF3GhpMiLhif7ms2IOs2n5IkimgCg==","signature_status":"signed_v1","signed_at":"2026-06-23T00:11:54.723719Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.20723","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0cb646d21973aceb59a3153ca4679ee52f2d71e9ba66cb697bc87f3e921b5ddb","sha256:6db43e71ab271d7c7e97e05ce1bbfa0b88856145f8fbd4b58e472e31c960e93f"],"state_sha256":"ff1cb09b30a45b12174c9cb2ccef5f8376f325e6f1d17d7b10d83424e24c54f4"}