{"id":"W4387331761","doi":"10.1016/j.orhc.2023.100409","title":"Health outcome predictive modelling in intensive care units","year":2023,"lang":"en","type":"article","venue":"Operations Research for Health Care","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"The King's University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Western University","keywords":"Medicine; Intensive care; Logistic regression; Workload; Multinomial logistic regression; Emergency medicine; APACHE II; Receiver operating characteristic; Intensive care medicine; Intensive care unit; Machine learning; Internal medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007527856,0.0001331429,0.0004132125,0.0007056537,0.0007465259,0.00004538815,0.00008638522,0.00007535307,0.00001461104],"category_scores_gemma":[0.0008156165,0.0001145315,0.00006548301,0.001493149,0.00005511295,0.00008339146,0.00006074557,0.00040186,0.00005923313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002271029,"about_ca_system_score_gemma":0.00296692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005627376,"about_ca_topic_score_gemma":0.009481604,"domain_scores_codex":[0.9975784,0.0002298807,0.0004816127,0.0004110902,0.0005262012,0.0007727669],"domain_scores_gemma":[0.9947105,0.0002739418,0.00002587614,0.0003430074,0.004258054,0.0003886162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0007509589,0.0003556907,0.1401282,0.005083817,0.0002266181,0.0001233252,0.4184805,0.2993455,0.000008394694,0.00458778,0.0890991,0.0418101],"study_design_scores_gemma":[0.007572403,0.008510729,0.05760792,0.001868649,0.00002725032,0.00001445426,0.7095011,0.1823406,0.0001169391,0.00006272004,0.03204682,0.000330422],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.734416,0.01917196,0.001742325,0.2256888,0.000475175,0.0152501,0.002278331,0.0002907323,0.000686595],"genre_scores_gemma":[0.989795,0.001524015,0.0007577835,0.002943795,0.0001100767,0.001971062,0.00250048,0.00003914362,0.00035866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2910206,"threshold_uncertainty_score":0.8506947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5293279258578945,"score_gpt":0.5813576479679834,"score_spread":0.05202972211008894,"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."}}