{"id":"W4289705447","doi":"10.1016/j.jacep.2022.05.003","title":"Use of Wearable Technology and Deep Learning to Improve the Diagnosis of Brugada Syndrome","year":2022,"lang":"en","type":"article","venue":"JACC. Clinical electrophysiology","topic":"Cardiac electrophysiology and arrhythmias","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Toronto General Hospital; University Health Network","funders":"","keywords":"Brugada syndrome; Medicine; Internal medicine; Cardiology; Precordial examination; Electrocardiography","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":[0.0004290162,0.0001922989,0.001093762,0.0002473221,0.0002275381,0.000003509135,0.0001954417,0.0002326245,0.0001371481],"category_scores_gemma":[0.001436119,0.0001449446,0.0002964786,0.0008640597,0.0005803886,0.00003837799,0.0004636431,0.001442424,0.00001013945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004038918,"about_ca_system_score_gemma":0.0001294015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007736353,"about_ca_topic_score_gemma":0.000004640146,"domain_scores_codex":[0.9975011,0.0005725349,0.0007443004,0.0005288983,0.000165471,0.0004876924],"domain_scores_gemma":[0.9972324,0.001689412,0.0003249607,0.0005248485,0.0001259143,0.000102472],"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":[0.005968338,0.0009918836,0.050009,0.000125964,0.001178248,0.0003514176,0.0001796876,0.001747075,0.8107551,0.003054129,0.0006551422,0.124984],"study_design_scores_gemma":[0.005967971,0.112004,0.7847834,0.0001464637,0.00123836,0.002972484,0.0006008518,0.001822685,0.05273777,0.01022799,0.02646591,0.001032092],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951439,0.001422176,0.0000230762,0.002599462,0.0001983691,0.0005275748,0.000007541316,0.00004482291,0.0000330749],"genre_scores_gemma":[0.9961285,0.001635964,0.0003417756,0.001233592,0.0000977487,0.0002417722,0.00000777831,0.00002827831,0.000284611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7580173,"threshold_uncertainty_score":0.6266696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01855982905544237,"score_gpt":0.3009879876194471,"score_spread":0.2824281585640048,"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."}}