{"id":"W2595542355","doi":"10.1016/s0735-1097(17)33409-5","title":"ARTIFICIAL INTELLIGENCE TO DIAGNOSE ACUTE CORONARY SYNDROMES: INSIGHTS FROM A META-ANALYSIS OF MACHINE LEARNING","year":2017,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"Medicine; Artificial intelligence; Machine learning; Meta-analysis; Internal medicine; Computer science","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":[0.0008793796,0.0002063998,0.002582909,0.0005079692,0.0002729831,0.00002853632,0.003466809,0.00005973,0.00002066336],"category_scores_gemma":[0.001452307,0.0001319934,0.001715501,0.0009531631,0.0004624162,0.0001880122,0.0009457693,0.0005995281,0.000002811203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006182985,"about_ca_system_score_gemma":0.000199902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006479634,"about_ca_topic_score_gemma":0.0001476134,"domain_scores_codex":[0.9964203,0.001392402,0.001071148,0.0003190326,0.000556296,0.0002408352],"domain_scores_gemma":[0.993193,0.00130372,0.003424179,0.001456916,0.000468094,0.0001540809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0008949551,0.000170039,0.2809067,0.00003913882,0.2909925,0.002461602,0.001875089,0.3803181,0.003088596,0.01299334,0.000541042,0.02571884],"study_design_scores_gemma":[0.0003290145,0.007021077,0.7237442,0.00009039239,0.1319537,0.001835531,0.0009394345,0.1053571,0.004430896,0.01918503,0.004108157,0.001005536],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8944361,0.001004272,0.09532574,0.008214318,0.0005614922,0.0001946732,0.000136556,0.00001264712,0.0001142307],"genre_scores_gemma":[0.9868408,0.0001114133,0.01264656,0.0002604808,0.00008731892,0.000005425382,9.912277e-7,0.00001134374,0.00003567403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4428374,"threshold_uncertainty_score":0.6442251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06719623025180395,"score_gpt":0.3454353998599496,"score_spread":0.2782391696081457,"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."}}