{"id":"W4311708632","doi":"10.3389/fcvm.2022.1050409","title":"Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease","year":2022,"lang":"en","type":"article","venue":"Frontiers in Cardiovascular Medicine","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)","keywords":"Medicine; Coronary artery disease; Internal medicine; Atrial fibrillation; Cardiology; Hazard ratio; Sleep apnea; Obstructive sleep apnea; Confidence interval","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":[0.002929544,0.0005312947,0.0007958391,0.001670171,0.0002993342,0.000701148,0.0005398878,0.0005790477,0.001318635],"category_scores_gemma":[0.01242125,0.0001684268,0.0007062551,0.0008958465,0.0002011,0.0004334108,0.0004864429,0.0008640853,0.0002885112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004720046,"about_ca_system_score_gemma":0.0007040956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00422006,"about_ca_topic_score_gemma":0.003294977,"domain_scores_codex":[0.9986812,0.0007429361,0.0001293993,0.0001884426,0.0001664247,0.0000914769],"domain_scores_gemma":[0.9928785,0.005549533,0.0006481317,0.0002279959,0.0004790365,0.0002168689],"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":[0.0007706838,0.0004048418,0.8603083,0.0001255484,0.0004813816,0.0001649077,0.00008872482,0.03451682,0.0003929408,0.0004258471,0.002493654,0.09982632],"study_design_scores_gemma":[0.00008928742,0.0005119637,0.2257417,0.0001141523,0.000219538,0.0004800001,0.0001289703,0.7673011,0.0005945858,0.003662696,0.001117767,0.00003815741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9437131,0.004597066,0.04349116,0.002433765,0.0002117041,0.0001932412,0.00229263,0.0003602329,0.002707187],"genre_scores_gemma":[0.9890726,0.0004820913,0.009164766,0.00009291749,0.0001100376,0.0000774046,0.0007462475,0.000005994266,0.0002479344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00422006,"threshold_uncertainty_score":0.01549309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007028118503407895,"score_gpt":0.2194210139776712,"score_spread":0.2123928954742633,"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."}}