{"id":"W4389730436","doi":"10.1097/01.ccm.0000999752.43143.ae","title":"393: MACHINE LEARNING MODELS FOR PREDICTING MORTALITY IN SEPSIS: A SYSTEMATIC REVIEW","year":2023,"lang":"en","type":"review","venue":"Critical Care Medicine","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Medicine; Sepsis; Intensive care medicine; Machine learning; Artificial intelligence; 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":[],"consensus_categories":[],"category_scores_codex":[0.01115407,0.002193947,0.01132294,0.009868667,0.0004421845,0.002209426,0.002663165,0.001716584,0.006636264],"category_scores_gemma":[0.0429425,0.0009346295,0.01602376,0.01080565,0.000530277,0.002634349,0.00116837,0.001862185,0.0007726369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00247793,"about_ca_system_score_gemma":0.006031623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01043404,"about_ca_topic_score_gemma":0.01758661,"domain_scores_codex":[0.9925538,0.003792316,0.001868838,0.000607172,0.001051122,0.0001266982],"domain_scores_gemma":[0.9619189,0.03136504,0.003535935,0.0005777055,0.002335981,0.0002665252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0007719498,0.00005673566,0.005051801,0.7739509,0.09271955,0.0001272217,0.00008331922,0.002084181,0.0001551745,0.0005343836,0.008148428,0.1163163],"study_design_scores_gemma":[0.001394609,0.0005944202,0.0178636,0.6033382,0.3334547,0.0004906037,0.0001959071,0.006623416,0.0002645999,0.002958495,0.03264224,0.0001792147],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008865611,0.9959387,0.0007932318,0.0005166943,0.0001263279,0.0001893401,0.001316386,0.00003646132,0.0001963293],"genre_scores_gemma":[0.03309115,0.9610515,0.002693413,0.0005570395,0.000280166,0.0006290913,0.001479657,0.00002823051,0.0001897755],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01132294,"threshold_uncertainty_score":0.05898905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2046477226719901,"score_gpt":0.4663283528588116,"score_spread":0.2616806301868216,"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."}}