{"id":"W3107448060","doi":"10.1093/ehjci/ehaa946.0432","title":"Machine learning for predicting AF ablation outcomes using daily heart rhythm data at baseline","year":2020,"lang":"en","type":"article","venue":"European Heart Journal","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Atrial fibrillation; Ablation; Catheter ablation; Cardiology; Internal medicine; Sinus rhythm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00661464,0.001209201,0.0009724573,0.002153361,0.000353237,0.001000568,0.0009282977,0.0009961041,0.00153153],"category_scores_gemma":[0.01540319,0.0002666565,0.001306843,0.0009990523,0.0003709281,0.0006403752,0.0006816894,0.001261421,0.0004515434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006451498,"about_ca_system_score_gemma":0.0009982064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005906649,"about_ca_topic_score_gemma":0.00310659,"domain_scores_codex":[0.9980143,0.001155218,0.0001404004,0.0003987542,0.0001381041,0.0001531134],"domain_scores_gemma":[0.983863,0.01361257,0.0008874015,0.0006652821,0.0005966138,0.0003751187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001501919,0.0009420571,0.4388306,0.0001108829,0.0009877315,0.0001570318,0.00008683823,0.4644904,0.0008666528,0.0004834652,0.001970767,0.08957171],"study_design_scores_gemma":[0.00003809317,0.0002991418,0.02844895,0.00002277145,0.0000610179,0.00004731657,0.00003359173,0.9697443,0.0002629793,0.0008609202,0.0001597797,0.00002110509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9425796,0.0008647678,0.05169092,0.0007337194,0.00008343794,0.0001291197,0.002657332,0.0004172255,0.0008438803],"genre_scores_gemma":[0.9885522,0.0001061923,0.009080634,0.00005717062,0.00003669823,0.00009366861,0.001810976,0.00001399951,0.0002484802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00661464,"threshold_uncertainty_score":0.03498197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2349467915292174,"score_gpt":0.3814626522016126,"score_spread":0.1465158606723952,"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."}}