{"id":"W4366241514","doi":"10.1111/anae.16024","title":"Machine learning to predict myocardial injury and death after non‐cardiac surgery","year":2023,"lang":"en","type":"article","venue":"Anaesthesia","topic":"Cardiac, Anesthesia and Surgical Outcomes","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Population Health Research Institute","funders":"British Heart Foundation","keywords":"Medicine; Receiver operating characteristic; Cardiac surgery; Internal medicine; Population; Cardiology; Area under the curve; Surgery","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":[0.000892195,0.0002623819,0.001053571,0.0003510818,0.0001031252,0.00004608533,0.00005781685,0.0001462528,0.00005235136],"category_scores_gemma":[0.0002529492,0.0002181413,0.0006231295,0.0005565576,0.00004485912,0.00008214004,0.00006763391,0.0002532903,0.0004620954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000035801,"about_ca_system_score_gemma":0.00007301644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009350463,"about_ca_topic_score_gemma":0.000001740036,"domain_scores_codex":[0.998194,0.0001624522,0.000303574,0.0004205881,0.0004217636,0.0004976545],"domain_scores_gemma":[0.9987577,0.0003827889,0.00004076659,0.0003210072,0.00004796038,0.0004498092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004099675,0.00001871233,0.9560609,0.00003740683,0.0001282229,0.001002973,0.0002290996,0.000004201833,0.00006516084,0.000111791,0.003081284,0.03885032],"study_design_scores_gemma":[0.000166793,0.0001817112,0.7068474,0.00005123738,0.0001338348,0.00004266288,0.00004450947,0.00007774747,0.00004933776,0.00000956314,0.2922099,0.0001852827],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916365,0.0006525849,0.00001194606,0.003124447,0.0001749131,0.000402272,0.00001186076,0.0003079046,0.003677567],"genre_scores_gemma":[0.9923154,0.00147907,0.000109832,0.001825209,0.0003838436,0.00009900398,0.00005798048,0.00006288709,0.003666755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2891286,"threshold_uncertainty_score":0.8895542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01198319481569034,"score_gpt":0.2514282198232347,"score_spread":0.2394450250075444,"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."}}