{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004763349,0.0007694657,0.005978487,0.0005008077,0.0002322773,0.00007325787,0.001851255,0.0004144598,0.00001564956],"category_scores_gemma":[0.03201288,0.0005601565,0.0005836418,0.001634612,0.0001526336,0.0002328255,0.0005737087,0.002191763,0.00003628775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004329856,"about_ca_system_score_gemma":0.0004385047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003702356,"about_ca_topic_score_gemma":0.00009445024,"domain_scores_codex":[0.9914966,0.002202421,0.002879736,0.001369439,0.001103382,0.0009484612],"domain_scores_gemma":[0.9901311,0.006815598,0.0005454512,0.00152754,0.0005642521,0.0004160847],"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":[5.206476e-7,0.00001017236,0.00002562905,0.9671326,0.000046139,0.00009810937,0.0004752749,0.00002456158,2.403921e-9,0.00747219,0.00007921978,0.02463553],"study_design_scores_gemma":[0.0001864826,0.0002363302,0.00000113159,0.9127298,0.001877539,0.00006885488,0.0001767532,0.07117299,5.68784e-9,0.0009784407,0.01213489,0.0004367739],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[1.223523e-7,0.9540469,0.03741005,0.001530934,0.0007968029,0.00518534,0.00004763137,0.0006914922,0.000290698],"genre_scores_gemma":[0.00007849039,0.9940175,0.001452244,0.0007099821,0.000409946,0.002837999,0.0002653001,0.0001293682,0.0000991835],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.07114843,"threshold_uncertainty_score":0.999685,"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."}}