{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003411512,0.0001135929,0.0009134433,0.0005258916,0.0005023112,0.000006877337,0.00009538871,0.0004653318,0.000009137945],"category_scores_gemma":[0.002912067,0.0001130106,0.0006078362,0.0002349997,0.0001672591,0.0001536991,0.00002664352,0.000799123,0.000001914148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002924162,"about_ca_system_score_gemma":0.004417597,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00903935,"about_ca_topic_score_gemma":0.004895243,"domain_scores_codex":[0.9953471,0.002328559,0.00160106,0.0001753461,0.0001284955,0.0004194692],"domain_scores_gemma":[0.9961225,0.002273817,0.0006254734,0.0001516394,0.00046785,0.0003586626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009101586,1.980527e-7,0.9606708,0.00005282776,0.0002240576,0.00001041306,0.0003837536,0.02170089,0.00001699234,0.0002488937,0.0006639633,0.01593622],"study_design_scores_gemma":[0.0009116209,0.0006293024,0.8134216,0.002258856,0.001371143,0.00007081204,0.010155,0.08108713,0.00004364277,0.01095267,0.07859537,0.0005028159],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9702874,0.01002777,0.009013766,0.0002954048,0.009523279,0.0002777798,0.00003100663,0.00000928176,0.0005342792],"genre_scores_gemma":[0.9954384,0.001105067,0.0001343532,0.00005072888,0.003206743,9.142163e-7,0.000007983199,0.00001469385,0.00004111687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1472491,"threshold_uncertainty_score":0.9975595,"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."}}