{"id":"W4414714150","doi":"10.1016/j.cjca.2025.08.089","title":"P123 PREDICTION OF ATRIAL FIBRILLATION FOLLOWING CARDIAC SURGERY USING DEEP LEARNING, CLINICAL MODELS, AND POLYGENIC SCORES","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Atrial fibrillation; Cardiac surgery; Complication; Modalities; Cardiac arrhythmia; Polygenic risk score","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0009913406,0.0005096491,0.0005383388,0.0007034413,0.0002642862,0.001075542,0.0004333024,0.0007518819,0.001604986],"category_scores_gemma":[0.005540667,0.0001842769,0.0007324559,0.0005813683,0.0002469287,0.0005379707,0.000495857,0.001167588,0.000418049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002998178,"about_ca_system_score_gemma":0.0005182405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005340231,"about_ca_topic_score_gemma":0.005617548,"domain_scores_codex":[0.9996461,0.00009279822,0.00003862323,0.0001073567,0.00005016622,0.00006502251],"domain_scores_gemma":[0.9973259,0.001570096,0.0004254381,0.0001769646,0.0002599008,0.0002416814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001254,0.0002578379,0.9395441,0.00004756625,0.0004378977,0.0004025339,0.00003483006,0.0145854,0.001265744,0.0004250481,0.001890887,0.03985408],"study_design_scores_gemma":[0.00008633583,0.0004051173,0.5745397,0.00004947378,0.0004765279,0.0008660727,0.00009032261,0.4175823,0.001171013,0.003903176,0.0007836195,0.00004630506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9890322,0.0004823999,0.007196926,0.0005712991,0.00006320455,0.00001209554,0.001696082,0.00008188564,0.0008640286],"genre_scores_gemma":[0.9974757,0.0001182694,0.0008694649,0.00003668101,0.00003720436,0.000007110191,0.00109849,0.000007236487,0.0003498763],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005340231,"threshold_uncertainty_score":0.01061827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2047449332205666,"score_gpt":0.4552934370024608,"score_spread":0.2505485037818942,"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."}}