{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001931953,0.001126183,0.001715563,0.001386296,0.0003005152,0.002248613,0.001533396,0.001939086,0.004275904],"category_scores_gemma":[0.005243043,0.0004333164,0.001297419,0.001249105,0.0008483499,0.002217981,0.001533528,0.00269656,0.001552739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007945634,"about_ca_system_score_gemma":0.002730124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002187744,"about_ca_topic_score_gemma":0.002080783,"domain_scores_codex":[0.9993988,0.0002803644,0.00006646151,0.00006001183,0.0001562415,0.00003817233],"domain_scores_gemma":[0.9974572,0.001988115,0.0001098447,0.00006538414,0.0003023263,0.00007700469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001068773,0.0001267682,0.0007423487,0.02435619,0.0004758833,0.0002654191,0.0002851667,0.01589123,0.0008089762,0.0966351,0.05073553,0.8095706],"study_design_scores_gemma":[0.00006014616,0.0001626125,0.0008752529,0.02137389,0.0004409156,0.0007221856,0.000290089,0.01011681,0.0005970192,0.103902,0.8613495,0.0001095737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003991489,0.9880996,0.003970789,0.004510062,0.0006016683,0.00001626883,0.00006618998,0.00003914042,0.002297136],"genre_scores_gemma":[0.003392814,0.9920986,0.00265377,0.0006405013,0.0005510504,0.00003568881,0.0001049809,0.00001824538,0.0005043887],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004275904,"threshold_uncertainty_score":0.0143044,"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."}}