{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001417856,0.0002078601,0.0006885645,0.0005994744,0.0001980244,0.000006999196,0.00007517095,0.00005860191,0.00004043966],"category_scores_gemma":[0.0005694469,0.0001802636,0.0002433199,0.0005068895,0.0001771319,0.0001193447,0.00009583709,0.0005944152,5.050656e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004667393,"about_ca_system_score_gemma":0.00004057776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007333881,"about_ca_topic_score_gemma":0.00000347959,"domain_scores_codex":[0.9971233,0.0004555811,0.0003718026,0.0005583656,0.001137101,0.0003538215],"domain_scores_gemma":[0.9991207,0.00009704776,0.00009509133,0.0003738368,0.0001057425,0.0002076259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006519828,0.00003418892,0.8579868,0.0001019329,0.0008245915,0.00007924098,0.0002661153,0.02040269,0.000001864683,0.000001993003,0.00008695541,0.1136938],"study_design_scores_gemma":[0.02366004,0.001448474,0.8670703,0.00004674853,0.0007234237,0.00002110263,0.0004442733,0.1020922,9.190481e-7,0.00005697245,0.004298102,0.0001374371],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9344578,0.03709498,0.02358206,0.0002547124,0.001112829,0.003229149,0.0001366933,0.00005733901,0.00007440972],"genre_scores_gemma":[0.9972587,0.000103764,0.001062726,0.00002010664,0.0004974703,0.0001067526,0.0008610021,0.00005147628,0.00003797666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1135563,"threshold_uncertainty_score":0.7350935,"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."}}