{"id":"W4410191686","doi":"10.1093/ehjdh/ztaf047","title":"A deep learning phenome wide association study of the electrocardiogram","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; Bristol-Myers Squibb Canada; Norges Idrettshøgskole; Johnson and Johnson; Boston Scientific Corporation; National Institutes of Health; Apple","keywords":"Phenome; Association (psychology); Artificial intelligence; Computer science; Medicine; Psychology; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00318502,0.0005933944,0.0005032368,0.0007504251,0.0003509742,0.0006825019,0.000623173,0.0007608649,0.0012246],"category_scores_gemma":[0.007208181,0.0002388289,0.0006265709,0.0006016157,0.0003321749,0.0004269264,0.0008732475,0.001507518,0.0002676872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004822042,"about_ca_system_score_gemma":0.0006745009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004407842,"about_ca_topic_score_gemma":0.005913613,"domain_scores_codex":[0.9989688,0.000489008,0.00004898886,0.0003019845,0.00009716042,0.0000940966],"domain_scores_gemma":[0.997604,0.001475355,0.0002148861,0.0003147213,0.0002023689,0.0001887516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001959987,0.001244585,0.8286561,0.0002031544,0.001601711,0.0007886782,0.0001651177,0.0457131,0.006634424,0.00135672,0.00680908,0.1048673],"study_design_scores_gemma":[0.0002619323,0.0007163866,0.2560744,0.00007674979,0.000459545,0.0007005834,0.0001342441,0.7304685,0.002720647,0.005668379,0.002673636,0.00004503818],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9689062,0.0007853586,0.02523259,0.001597843,0.00009255722,0.00005955678,0.002257595,0.0001737901,0.000894428],"genre_scores_gemma":[0.9905499,0.0001659055,0.006127136,0.0002114267,0.00004375663,0.00003417989,0.002279193,0.00001049756,0.0005780054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004407842,"threshold_uncertainty_score":0.01684421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01768993546488477,"score_gpt":0.3133076825284766,"score_spread":0.2956177470635918,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}