{"id":"W4398769767","doi":"10.2196/54872","title":"Development and Validation of an Explainable Machine Learning Model for Predicting Myocardial Injury After Noncardiac Surgery in Two Centers in China: Retrospective Study","year":2024,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Cardiac, Anesthesia and Surgical Outcomes","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Beijing Nova Program","keywords":"Preprint; China; Medicine; Cardiac surgery; Internal medicine; Computer science; History","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007219099,0.0008164168,0.0007910606,0.001686678,0.0005327587,0.0007725478,0.001035625,0.0006118285,0.0006573478],"category_scores_gemma":[0.008621798,0.0003957189,0.001376852,0.001004358,0.0003638254,0.0004987526,0.0008535156,0.0005564266,0.0001520935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001092621,"about_ca_system_score_gemma":0.002005293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01925872,"about_ca_topic_score_gemma":0.01199526,"domain_scores_codex":[0.998427,0.0005623968,0.0001699684,0.0003795552,0.0002928981,0.000168122],"domain_scores_gemma":[0.9950969,0.001832534,0.000751474,0.0007870455,0.001227083,0.0003050515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001361641,0.0001591721,0.9842702,0.00002152552,0.0001572319,0.0002437046,0.0002163279,0.006570356,0.00025992,0.00009442019,0.0002804245,0.007590502],"study_design_scores_gemma":[0.00006990466,0.0004651832,0.7743308,0.00003614893,0.0003236721,0.0003065924,0.0005498891,0.2220703,0.0007886732,0.0003145018,0.0007097679,0.00003461347],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962603,0.00005308211,0.003208533,0.00004594811,0.00000527475,0.00005376706,0.0002666057,0.0000139553,0.00009268028],"genre_scores_gemma":[0.9959155,0.00005521324,0.002663631,0.00002326852,0.00001001983,0.00007749874,0.001122078,0.000005446857,0.000127404],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01925872,"threshold_uncertainty_score":0.03829324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01357637089536483,"score_gpt":0.3006090965213278,"score_spread":0.287032725625963,"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."}}