{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:I5P4WB5BHWGNV76VRFHGSEMJQU","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":"06ca702e41730120351c3ac3c72ca4ae62a30c02e322312aba6a8964c29e3e60","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-21T15:41:20Z","title_canon_sha256":"096305f34ec5ec7333f7e168f95762c57604ac091accda224d52f2e67022c631"},"schema_version":"1.0","source":{"id":"2502.00025","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.00025","created_at":"2026-07-05T11:32:24Z"},{"alias_kind":"arxiv_version","alias_value":"2502.00025v4","created_at":"2026-07-05T11:32:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00025","created_at":"2026-07-05T11:32:24Z"},{"alias_kind":"pith_short_12","alias_value":"I5P4WB5BHWGN","created_at":"2026-07-05T11:32:24Z"},{"alias_kind":"pith_short_16","alias_value":"I5P4WB5BHWGNV76V","created_at":"2026-07-05T11:32:24Z"},{"alias_kind":"pith_short_8","alias_value":"I5P4WB5B","created_at":"2026-07-05T11:32:24Z"}],"graph_snapshots":[{"event_id":"sha256:85bb83327085659e2e2c78835741ba52a0d04e1303f100489cbc8d158747182e","target":"graph","created_at":"2026-07-05T11:32:24Z","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/2502.00025/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Importance: Emergency department (ED) returns for mental health conditions pose a major healthcare burden, with 24-27% of patients returning within 30 days. Traditional machine learning models for predicting these returns often lack interpretability for clinical use.\n  Objective: To assess whether integrating large language models (LLMs) with machine learning improves predictive accuracy and clinical interpretability of ED mental health return risk models.\n  Methods: This retrospective cohort study analyzed 42,464 ED visits for 27,904 unique mental health patients at an academic medical center","authors_text":"Abdulaziz Ahmed, Ahmed Alhassan, Badari Birur, Bradley G Burk, Mohammad Saleem, Mohammed Ali Al-Garadi, Mohammed Alzeen, Rachel E Fargason","cross_cats":["cs.AI","cs.CY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-21T15:41:20Z","title":"Explainable AI for Mental Health Emergency Returns: Integrating LLMs with Predictive Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00025","kind":"arxiv","version":4},"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:d1d61da032b24b912bbe4d6515fbf7354c5919f99954ca2256e559147d5e9b16","target":"record","created_at":"2026-07-05T11:32:24Z","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":"06ca702e41730120351c3ac3c72ca4ae62a30c02e322312aba6a8964c29e3e60","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-21T15:41:20Z","title_canon_sha256":"096305f34ec5ec7333f7e168f95762c57604ac091accda224d52f2e67022c631"},"schema_version":"1.0","source":{"id":"2502.00025","kind":"arxiv","version":4}},"canonical_sha256":"475fcb07a13d8cdaffd5894e69118985084e5554a8c5799d3dd8c91f733f3e65","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"475fcb07a13d8cdaffd5894e69118985084e5554a8c5799d3dd8c91f733f3e65","first_computed_at":"2026-07-05T11:32:24.034474Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:32:24.034474Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xZWVL+hkesmH/eVwwm9Ugi0MNN12L5jpCYIsYfpusj83BJcqq622H3baHKhoy/vVGy8/rTKZlnapmEfhq13wDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:32:24.034984Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.00025","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d1d61da032b24b912bbe4d6515fbf7354c5919f99954ca2256e559147d5e9b16","sha256:85bb83327085659e2e2c78835741ba52a0d04e1303f100489cbc8d158747182e"],"state_sha256":"610d479590708bd993b23247f31ebf624dab005cba0cdf8b2850b31c1fa8cb67"}