{"id":"W4407342702","doi":"10.1161/circep.124.012860","title":"Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning","year":2025,"lang":"en","type":"article","venue":"Circulation Arrhythmia and Electrophysiology","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Heart, Lung, and Blood Institute","keywords":"Medicine; Atrial fibrillation; Ablation; Cohort; Logistic regression; Internal medicine; Cardiology; Catheter ablation; Population","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001674372,0.0001297748,0.0003235432,0.0002683164,0.0002571063,0.00003250176,0.00001651357,0.00008831693,0.000007921351],"category_scores_gemma":[0.0001808502,0.0001282165,0.00008127475,0.0002196899,0.00002978884,0.0000936134,0.0000340493,0.00007341496,0.000002257174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007084871,"about_ca_system_score_gemma":0.00003917659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007709383,"about_ca_topic_score_gemma":0.000003920542,"domain_scores_codex":[0.9991226,0.00008991411,0.0002514311,0.000281507,0.00006800747,0.0001865417],"domain_scores_gemma":[0.9994688,0.0002067589,0.00009591421,0.00009393671,0.00008001745,0.00005458642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00975488,0.000001030006,0.4528084,0.00008735525,0.000278622,0.000002531179,0.0001181617,0.02137867,0.4998723,0.002351897,0.000004131312,0.01334203],"study_design_scores_gemma":[0.001930368,0.0002340335,0.7954974,0.00003479848,0.0003232432,0.00001627247,0.000006025227,0.1998858,0.0005446483,0.0005839898,0.0008217216,0.0001217487],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.962718,0.0003758697,0.03466928,0.00132032,0.0001435903,0.0006854254,0.000001267395,0.0000582885,0.00002803769],"genre_scores_gemma":[0.9974503,0.00007495515,0.001218939,0.0001017017,0.0007845967,0.00000202048,0.00009367404,0.00001327933,0.0002605545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4993276,"threshold_uncertainty_score":0.5228518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03461822594320579,"score_gpt":0.3372410924597566,"score_spread":0.3026228665165508,"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."}}