{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FVJUPPJEMOYNDEXFSM4SJ5FP5W","short_pith_number":"pith:FVJUPPJE","schema_version":"1.0","canonical_sha256":"2d5347bd2463b0d192e5933924f4afed9eacb39327b9071ec525b92e818fd91b","source":{"kind":"arxiv","id":"2308.12224","version":1},"attestation_state":"computed","paper":{"title":"Enhancing cardiovascular risk prediction through AI-enabled calcium-omics","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"q-bio.QM","authors_text":"Ammar Hoori, David L. Wilson, Hao Wu, Juhwan Lee, Nour Tashtish, Pingfu Fu, Robert Gilkeson, Sadeer Al-Kindi, Sanjay Rajagopalan, Tao Hu, Yingnan Song","submitted_at":"2023-08-23T16:05:14Z","abstract_excerpt":"Background. Coronary artery calcium (CAC) is a powerful predictor of major adverse cardiovascular events (MACE). Traditional Agatston score simply sums the calcium, albeit in a non-linear way, leaving room for improved calcification assessments that will more fully capture the extent of disease.\n  Objective. To determine if AI methods using detailed calcification features (i.e., calcium-omics) can improve MACE prediction.\n  Methods. We investigated additional features of calcification including assessment of mass, volume, density, spatial distribution, territory, etc. We used a Cox model with "},"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":"2308.12224","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"q-bio.QM","submitted_at":"2023-08-23T16:05:14Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1a5462cdb2a8d55bf7b3c74d2c02fd2639e009466ce1035af7343ea6e4e89e37","abstract_canon_sha256":"d3421e25d1bde09c0e10de68ac5db029972682beab249400a5e6cb5341c90f64"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T00:13:43.075563Z","signature_b64":"LwZR/m/4IEPf97CuNSLJ1lBibe8eV9x0/q5KvPAAM+jedDTcfe8eDizyYGfMBJdO3c+uVTIQasU5Y/wJPwxLDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2d5347bd2463b0d192e5933924f4afed9eacb39327b9071ec525b92e818fd91b","last_reissued_at":"2026-06-05T00:13:43.074921Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T00:13:43.074921Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing cardiovascular risk prediction through AI-enabled calcium-omics","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"q-bio.QM","authors_text":"Ammar Hoori, David L. Wilson, Hao Wu, Juhwan Lee, Nour Tashtish, Pingfu Fu, Robert Gilkeson, Sadeer Al-Kindi, Sanjay Rajagopalan, Tao Hu, Yingnan Song","submitted_at":"2023-08-23T16:05:14Z","abstract_excerpt":"Background. Coronary artery calcium (CAC) is a powerful predictor of major adverse cardiovascular events (MACE). Traditional Agatston score simply sums the calcium, albeit in a non-linear way, leaving room for improved calcification assessments that will more fully capture the extent of disease.\n  Objective. To determine if AI methods using detailed calcification features (i.e., calcium-omics) can improve MACE prediction.\n  Methods. We investigated additional features of calcification including assessment of mass, volume, density, spatial distribution, territory, etc. We used a Cox model with "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.12224","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/2308.12224/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":"2308.12224","created_at":"2026-06-05T00:13:43.074997+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.12224v1","created_at":"2026-06-05T00:13:43.074997+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.12224","created_at":"2026-06-05T00:13:43.074997+00:00"},{"alias_kind":"pith_short_12","alias_value":"FVJUPPJEMOYN","created_at":"2026-06-05T00:13:43.074997+00:00"},{"alias_kind":"pith_short_16","alias_value":"FVJUPPJEMOYNDEXF","created_at":"2026-06-05T00:13:43.074997+00:00"},{"alias_kind":"pith_short_8","alias_value":"FVJUPPJE","created_at":"2026-06-05T00:13:43.074997+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/FVJUPPJEMOYNDEXFSM4SJ5FP5W","json":"https://pith.science/pith/FVJUPPJEMOYNDEXFSM4SJ5FP5W.json","graph_json":"https://pith.science/api/pith-number/FVJUPPJEMOYNDEXFSM4SJ5FP5W/graph.json","events_json":"https://pith.science/api/pith-number/FVJUPPJEMOYNDEXFSM4SJ5FP5W/events.json","paper":"https://pith.science/paper/FVJUPPJE"},"agent_actions":{"view_html":"https://pith.science/pith/FVJUPPJEMOYNDEXFSM4SJ5FP5W","download_json":"https://pith.science/pith/FVJUPPJEMOYNDEXFSM4SJ5FP5W.json","view_paper":"https://pith.science/paper/FVJUPPJE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.12224&json=true","fetch_graph":"https://pith.science/api/pith-number/FVJUPPJEMOYNDEXFSM4SJ5FP5W/graph.json","fetch_events":"https://pith.science/api/pith-number/FVJUPPJEMOYNDEXFSM4SJ5FP5W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FVJUPPJEMOYNDEXFSM4SJ5FP5W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FVJUPPJEMOYNDEXFSM4SJ5FP5W/action/storage_attestation","attest_author":"https://pith.science/pith/FVJUPPJEMOYNDEXFSM4SJ5FP5W/action/author_attestation","sign_citation":"https://pith.science/pith/FVJUPPJEMOYNDEXFSM4SJ5FP5W/action/citation_signature","submit_replication":"https://pith.science/pith/FVJUPPJEMOYNDEXFSM4SJ5FP5W/action/replication_record"}},"created_at":"2026-06-05T00:13:43.074997+00:00","updated_at":"2026-06-05T00:13:43.074997+00:00"}