{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004561612,0.0007627757,0.001043718,0.0007017587,0.0004059558,0.001697942,0.001269641,0.0009419454,0.002468779],"category_scores_gemma":[0.01915668,0.0004577366,0.0009684404,0.0007560909,0.0003652667,0.000891666,0.0007757538,0.001856273,0.0003600483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001273795,"about_ca_system_score_gemma":0.001430117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03157831,"about_ca_topic_score_gemma":0.01433845,"domain_scores_codex":[0.9986982,0.0007940601,0.00005917152,0.0001967757,0.00009852157,0.0001531662],"domain_scores_gemma":[0.9868799,0.01150682,0.0004508281,0.0003216842,0.0005317933,0.0003089464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001089905,0.0004761541,0.04665719,0.000108126,0.0002529876,0.0002260992,0.0001732692,0.9043484,0.0002460994,0.003283019,0.003099075,0.0400396],"study_design_scores_gemma":[0.00001217605,0.00004274451,0.003283019,0.00001161444,0.00001651398,0.00001517984,0.00003102537,0.9941678,0.00008473856,0.002217311,0.0001110116,0.000006822177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8643596,0.001316022,0.1268739,0.00266871,0.000207758,0.0001525363,0.001523577,0.0005231924,0.00237473],"genre_scores_gemma":[0.990882,0.0002412832,0.006728416,0.00008508195,0.00004502696,0.00007388031,0.0008954505,0.00002514792,0.001023711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03157831,"threshold_uncertainty_score":0.06278896,"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."}}