{"id":"W4403808545","doi":"10.1093/eurheartj/ehae666.3479","title":"Incident atrial fibrillation prediction using ECG-based deep learning at a specialized tertiary cardiac care center","year":2024,"lang":"en","type":"article","venue":"European Heart Journal","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Heart Institute","funders":"","keywords":"Medicine; Atrial fibrillation; Tertiary care; Cardiology; Internal medicine; Cardiac arrhythmia; Center (category theory); Medical emergency; Emergency medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000648107,0.0002520555,0.000376978,0.0008275515,0.0001661679,0.000557815,0.0004872938,0.0002799843,0.001855455],"category_scores_gemma":[0.003455864,0.0001394644,0.0002720459,0.0008035325,0.0001363899,0.0002831079,0.0006927167,0.0004573663,0.0004098008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009068223,"about_ca_system_score_gemma":0.0008108867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01499692,"about_ca_topic_score_gemma":0.01868568,"domain_scores_codex":[0.999503,0.0001136675,0.00005075795,0.0001659791,0.00006637943,0.0001002019],"domain_scores_gemma":[0.9980117,0.0005641484,0.0004826347,0.0001272119,0.0004142016,0.0004000632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000210352,0.0001043137,0.9824508,0.00002371539,0.00005266202,0.0001910743,0.00003292995,0.005322352,0.0004872014,0.00004594511,0.0008344433,0.01024415],"study_design_scores_gemma":[0.00008137883,0.0004904459,0.7648671,0.00006903186,0.000126176,0.000482428,0.0002712035,0.23054,0.001976796,0.0004277811,0.000646757,0.00002096789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959517,0.00009966412,0.00173247,0.0001801936,0.00000720762,0.00002032158,0.001427265,0.00004275848,0.0005384212],"genre_scores_gemma":[0.9979053,0.00003537029,0.0008332944,0.00003648659,0.00001112495,0.000009109502,0.001029713,0.000002052971,0.0001375271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01499692,"threshold_uncertainty_score":0.02981925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02898075349513959,"score_gpt":0.3059872140281401,"score_spread":0.2770064605330005,"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."}}