{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:P24FQTHQGHVQPM6WL3QD7HW73B","short_pith_number":"pith:P24FQTHQ","schema_version":"1.0","canonical_sha256":"7eb8584cf031eb07b3d65ee03f9edfd84134f3e8d113fdcfad13bf77fa52c125","source":{"kind":"arxiv","id":"2607.15721","version":1},"attestation_state":"computed","paper":{"title":"CardioMeta: Calibrated Multi-Task Prediction of Diabetes, Hypertension, and Cardiovascular Disease Across Population and EHR Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jungpil Shin, M. F. Mridha, Ruksat Khan Shayoni, S M Asif Hossain","submitted_at":"2026-07-17T07:55:43Z","abstract_excerpt":"Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because diabetes, hypertension, and cardiovascular disease frequently co-occur and share metabolic, vascular, demographic, and behavioral determinants. Existing machine learning studies for chronic disease prediction often emphasize discrimination on a single dataset, while underreporting label leakage, calibration, temporal robustness, external transportability, and subgroup reliability. This paper presents CardioMeta, a calibrated multi-task framework for joint prediction of diabetes, hypertension, and"},"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":"2607.15721","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-17T07:55:43Z","cross_cats_sorted":[],"title_canon_sha256":"e4a1e104c77ee2e77f167700da60d0fbfa7d4ed583800fb53fa791074d485deb","abstract_canon_sha256":"4bfbf350998399bd939ed4fb657854d8a5bb8fa05e2cc2ab5f1e073086790bcb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-20T01:19:06.149529Z","signature_b64":"1rRjH6+7qx0McT5VV4aZAoMTrGC2zbCZqPDIAgzK8EJzYwSoB/mosjEO6EPWZtauul9kaEpCtDfz1SFVJpUhAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7eb8584cf031eb07b3d65ee03f9edfd84134f3e8d113fdcfad13bf77fa52c125","last_reissued_at":"2026-07-20T01:19:06.148693Z","signature_status":"signed_v1","first_computed_at":"2026-07-20T01:19:06.148693Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CardioMeta: Calibrated Multi-Task Prediction of Diabetes, Hypertension, and Cardiovascular Disease Across Population and EHR Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jungpil Shin, M. F. Mridha, Ruksat Khan Shayoni, S M Asif Hossain","submitted_at":"2026-07-17T07:55:43Z","abstract_excerpt":"Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because diabetes, hypertension, and cardiovascular disease frequently co-occur and share metabolic, vascular, demographic, and behavioral determinants. Existing machine learning studies for chronic disease prediction often emphasize discrimination on a single dataset, while underreporting label leakage, calibration, temporal robustness, external transportability, and subgroup reliability. This paper presents CardioMeta, a calibrated multi-task framework for joint prediction of diabetes, hypertension, and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.15721","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/2607.15721/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":"2607.15721","created_at":"2026-07-20T01:19:06.149113+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.15721v1","created_at":"2026-07-20T01:19:06.149113+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.15721","created_at":"2026-07-20T01:19:06.149113+00:00"},{"alias_kind":"pith_short_12","alias_value":"P24FQTHQGHVQ","created_at":"2026-07-20T01:19:06.149113+00:00"},{"alias_kind":"pith_short_16","alias_value":"P24FQTHQGHVQPM6W","created_at":"2026-07-20T01:19:06.149113+00:00"},{"alias_kind":"pith_short_8","alias_value":"P24FQTHQ","created_at":"2026-07-20T01:19:06.149113+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/P24FQTHQGHVQPM6WL3QD7HW73B","json":"https://pith.science/pith/P24FQTHQGHVQPM6WL3QD7HW73B.json","graph_json":"https://pith.science/api/pith-number/P24FQTHQGHVQPM6WL3QD7HW73B/graph.json","events_json":"https://pith.science/api/pith-number/P24FQTHQGHVQPM6WL3QD7HW73B/events.json","paper":"https://pith.science/paper/P24FQTHQ"},"agent_actions":{"view_html":"https://pith.science/pith/P24FQTHQGHVQPM6WL3QD7HW73B","download_json":"https://pith.science/pith/P24FQTHQGHVQPM6WL3QD7HW73B.json","view_paper":"https://pith.science/paper/P24FQTHQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.15721&json=true","fetch_graph":"https://pith.science/api/pith-number/P24FQTHQGHVQPM6WL3QD7HW73B/graph.json","fetch_events":"https://pith.science/api/pith-number/P24FQTHQGHVQPM6WL3QD7HW73B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P24FQTHQGHVQPM6WL3QD7HW73B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P24FQTHQGHVQPM6WL3QD7HW73B/action/storage_attestation","attest_author":"https://pith.science/pith/P24FQTHQGHVQPM6WL3QD7HW73B/action/author_attestation","sign_citation":"https://pith.science/pith/P24FQTHQGHVQPM6WL3QD7HW73B/action/citation_signature","submit_replication":"https://pith.science/pith/P24FQTHQGHVQPM6WL3QD7HW73B/action/replication_record"}},"created_at":"2026-07-20T01:19:06.149113+00:00","updated_at":"2026-07-20T01:19:06.149113+00:00"}