{"id":"W2890253608","doi":"10.1093/eurheartj/ehy565.2160","title":"2160Performance of a machine learning model vs. IMPROVE score for VTE prediction in acute medically ill patients: insights from the APEX trial","year":2018,"lang":"en","type":"article","venue":"European Heart Journal","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Medicine; Apex (geometry); Internal medicine; Intensive care medicine; Artificial intelligence; Machine learning","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":[0.0111775,0.0007972494,0.001889647,0.0001946312,0.0001773624,0.001210816,0.0005123556,0.001021296,0.002394137],"category_scores_gemma":[0.01290816,0.000216724,0.001864244,0.0002649808,0.0004683415,0.0009950518,0.0006755785,0.002187657,0.0003938107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003921881,"about_ca_system_score_gemma":0.0004351696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002232784,"about_ca_topic_score_gemma":0.000276194,"domain_scores_codex":[0.9962693,0.002862759,0.0001661726,0.0003205188,0.0002629906,0.0001183009],"domain_scores_gemma":[0.9935935,0.004235809,0.000871321,0.0006970887,0.0002623643,0.000339998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"observational","study_design_scores_codex":[0.8262435,0.003544003,0.05127566,0.001344973,0.01033684,0.0001069232,0.0001240132,0.02008669,0.001865946,0.001836906,0.005587462,0.0776471],"study_design_scores_gemma":[0.417961,0.2498758,0.09914837,0.0007795067,0.0280187,0.0004154832,0.0002238785,0.1789267,0.005303218,0.009860784,0.009235944,0.0002506869],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844005,0.004907783,0.00334624,0.001886075,0.0002856449,0.0004990537,0.001245454,0.00009143028,0.003337878],"genre_scores_gemma":[0.9937304,0.0007612357,0.002267021,0.000504342,0.0003563089,0.0003514345,0.001461018,0.00001764717,0.0005506511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0111775,"threshold_uncertainty_score":0.05911297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02680494261410818,"score_gpt":0.2738810808076975,"score_spread":0.2470761381935893,"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."}}