{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaepi_broad"],"consensus_categories":[],"category_scores_codex":[0.03373785,0.002457185,0.00785493,0.005160852,0.0007809186,0.004090862,0.002420648,0.00205815,0.001714133],"category_scores_gemma":[0.07712247,0.001132297,0.02750615,0.005249314,0.0008396043,0.002112279,0.00182902,0.002842522,0.0002162768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001154874,"about_ca_system_score_gemma":0.001579947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003726089,"about_ca_topic_score_gemma":0.004913102,"domain_scores_codex":[0.9678307,0.02091715,0.003870595,0.004285886,0.002569456,0.0005261388],"domain_scores_gemma":[0.8635158,0.1175393,0.006893876,0.008521493,0.002646874,0.000882701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.002700983,0.00005129866,0.07088256,0.004690014,0.9066907,0.00008609494,0.00009442739,0.001545309,0.000272425,0.0003367253,0.0004457708,0.01220362],"study_design_scores_gemma":[0.0007560647,0.0005458997,0.0390187,0.001423413,0.9506449,0.0002200459,0.00007531702,0.002551976,0.000338481,0.00276323,0.001614203,0.00004795228],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.1870727,0.7931588,0.01166736,0.003315783,0.0007419722,0.0001557043,0.002099622,0.0001538213,0.001634218],"genre_scores_gemma":[0.9391297,0.05092106,0.005980343,0.001340628,0.0005843181,0.0001001363,0.001640846,0.00008752698,0.0002154851],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9921451,"threshold_uncertainty_score":0.178425,"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."}}