{"id":"W3153519718","doi":"10.1093/cvr/cvab138","title":"Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care","year":2021,"lang":"en","type":"review","venue":"Cardiovascular Research","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; Montreal Heart Institute","funders":"Canadian Institutes of Health Research; National Institutes of Health; National Heart, Lung, and Blood Institute; Heart and Stroke Foundation of Canada; Hartstichting; ZonMw; Fondation Leducq","keywords":"Atrial fibrillation; Cardiac electrophysiology; Intensive care medicine; Computational model; Narrative review; Clinical trial; Medicine; Risk analysis (engineering); Disease; Personalized medicine; Psychological intervention; Computer science; Bioinformatics; Cardiology; Internal medicine; Artificial intelligence; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003983978,0.0002669628,0.002795211,0.0003985135,0.0001529344,0.0000436856,0.0001286608,0.0003561391,0.00001129928],"category_scores_gemma":[0.001042145,0.0002312252,0.005138699,0.0003021207,0.0002023667,0.00009442577,0.0003416208,0.0003998412,0.000003244226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001730921,"about_ca_system_score_gemma":0.0007289496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008060158,"about_ca_topic_score_gemma":6.613373e-7,"domain_scores_codex":[0.9958111,0.0009849553,0.0008574422,0.0008016446,0.001229862,0.0003150047],"domain_scores_gemma":[0.9963084,0.00131846,0.0001739638,0.0006472452,0.001408639,0.0001432682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005222057,0.000003111521,0.00002722778,0.02565,0.005238523,0.00001027383,0.000290079,0.0004083647,9.553667e-9,0.004368902,0.00003914522,0.9639121],"study_design_scores_gemma":[0.002091729,0.0002277834,0.00007509517,0.002710368,0.003915041,0.00001935417,0.001749966,0.0005241305,7.52375e-8,0.0002115979,0.9882628,0.0002121109],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001341956,0.9941902,0.001658358,0.0001039264,0.0001990949,0.003212869,0.00004305015,0.00002339738,0.0005556618],"genre_scores_gemma":[0.0002318545,0.9917248,0.002864442,0.000001433465,0.004413898,0.00001581508,0.0004966696,0.00006209571,0.0001889755],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9882236,"threshold_uncertainty_score":0.9429089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5273086976870549,"score_gpt":0.5086562772725082,"score_spread":0.01865242041454673,"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."}}