{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LDRB2K4MBXYMHSHYRMMU674S54","short_pith_number":"pith:LDRB2K4M","schema_version":"1.0","canonical_sha256":"58e21d2b8c0df0c3c8f88b194f7f92ef3a006750126872cb38dc704eab9b41e9","source":{"kind":"arxiv","id":"2606.28179","version":1},"attestation_state":"computed","paper":{"title":"CPAgents: Agentic Composite Phenotype Generation for Cardiac Disease Association","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bernhard Kainz, Kelly Yu, Mengyun Qiao, Paul M. Matthews, Weitong Zhang, Wenjia Bai, Wenlong Zhao, Zuoou Li","submitted_at":"2026-06-26T15:20:08Z","abstract_excerpt":"Identifying robust associations between cardiac imaging phenotypes and clinical diseases is fundamental to population-scale cardiovascular research and reliable risk stratification. However, current phenome-wide association studies rely on pre-defined, single-variable phenotypes or expert-crafted features, which limits their ability to capture clinically meaningful non-linear effects and cross-phenotype interactions. To address this, we propose CPAgents, an iterative phenotype-Composition framework for cardiovascular Phenome-wide association study (PheWAS) that automatically constructs and val"},"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":"2606.28179","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-26T15:20:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a409b66b17c5438b8253a789a8511f3ff593b503319d7556711a788bc5e651bd","abstract_canon_sha256":"ed21bdd3d1df668a30f43ad8e73c10d329f81a59afb02b2e511eea0428067af9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-29T01:15:08.427868Z","signature_b64":"CjzpQQ/CP+RBHsP3HF1RPXyfC4JTK7bqHm1E7lVNpjzU4v8Rf4qUGm9+e6eGDbT6dTMD4dpkOH6VnbrdLohfCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"58e21d2b8c0df0c3c8f88b194f7f92ef3a006750126872cb38dc704eab9b41e9","last_reissued_at":"2026-06-29T01:15:08.427460Z","signature_status":"signed_v1","first_computed_at":"2026-06-29T01:15:08.427460Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CPAgents: Agentic Composite Phenotype Generation for Cardiac Disease Association","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bernhard Kainz, Kelly Yu, Mengyun Qiao, Paul M. Matthews, Weitong Zhang, Wenjia Bai, Wenlong Zhao, Zuoou Li","submitted_at":"2026-06-26T15:20:08Z","abstract_excerpt":"Identifying robust associations between cardiac imaging phenotypes and clinical diseases is fundamental to population-scale cardiovascular research and reliable risk stratification. However, current phenome-wide association studies rely on pre-defined, single-variable phenotypes or expert-crafted features, which limits their ability to capture clinically meaningful non-linear effects and cross-phenotype interactions. To address this, we propose CPAgents, an iterative phenotype-Composition framework for cardiovascular Phenome-wide association study (PheWAS) that automatically constructs and val"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.28179","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/2606.28179/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":"2606.28179","created_at":"2026-06-29T01:15:08.427517+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.28179v1","created_at":"2026-06-29T01:15:08.427517+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.28179","created_at":"2026-06-29T01:15:08.427517+00:00"},{"alias_kind":"pith_short_12","alias_value":"LDRB2K4MBXYM","created_at":"2026-06-29T01:15:08.427517+00:00"},{"alias_kind":"pith_short_16","alias_value":"LDRB2K4MBXYMHSHY","created_at":"2026-06-29T01:15:08.427517+00:00"},{"alias_kind":"pith_short_8","alias_value":"LDRB2K4M","created_at":"2026-06-29T01:15:08.427517+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/LDRB2K4MBXYMHSHYRMMU674S54","json":"https://pith.science/pith/LDRB2K4MBXYMHSHYRMMU674S54.json","graph_json":"https://pith.science/api/pith-number/LDRB2K4MBXYMHSHYRMMU674S54/graph.json","events_json":"https://pith.science/api/pith-number/LDRB2K4MBXYMHSHYRMMU674S54/events.json","paper":"https://pith.science/paper/LDRB2K4M"},"agent_actions":{"view_html":"https://pith.science/pith/LDRB2K4MBXYMHSHYRMMU674S54","download_json":"https://pith.science/pith/LDRB2K4MBXYMHSHYRMMU674S54.json","view_paper":"https://pith.science/paper/LDRB2K4M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.28179&json=true","fetch_graph":"https://pith.science/api/pith-number/LDRB2K4MBXYMHSHYRMMU674S54/graph.json","fetch_events":"https://pith.science/api/pith-number/LDRB2K4MBXYMHSHYRMMU674S54/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LDRB2K4MBXYMHSHYRMMU674S54/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LDRB2K4MBXYMHSHYRMMU674S54/action/storage_attestation","attest_author":"https://pith.science/pith/LDRB2K4MBXYMHSHYRMMU674S54/action/author_attestation","sign_citation":"https://pith.science/pith/LDRB2K4MBXYMHSHYRMMU674S54/action/citation_signature","submit_replication":"https://pith.science/pith/LDRB2K4MBXYMHSHYRMMU674S54/action/replication_record"}},"created_at":"2026-06-29T01:15:08.427517+00:00","updated_at":"2026-06-29T01:15:08.427517+00:00"}