{"id":"W4311684734","doi":"10.1101/2022.12.15.22283527","title":"Health Outcome Predictive Modelling in Intensive Care Units","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"The King's University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Medicine; Logistic regression; Intensive care; Workload; Multinomial logistic regression; Emergency medicine; APACHE II; Receiver operating characteristic; Triage; Categorical variable; Intensive care medicine; Intensive care unit; Machine learning; Internal medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002726943,0.0006856518,0.0005949005,0.001076976,0.0003900597,0.00100425,0.001228637,0.0007126691,0.002082243],"category_scores_gemma":[0.009838905,0.0002678991,0.0007412558,0.001186223,0.0003031115,0.000461063,0.0006886458,0.001039276,0.0003550654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002347515,"about_ca_system_score_gemma":0.001590965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1473869,"about_ca_topic_score_gemma":0.07622714,"domain_scores_codex":[0.9989218,0.0004884047,0.00006577851,0.0002504071,0.0001446263,0.000128994],"domain_scores_gemma":[0.9951934,0.003302428,0.0003873168,0.0001470333,0.0008053047,0.0001644374],"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":[0.0002032887,0.0001931248,0.06669324,0.0001082852,0.0001402195,0.0002351561,0.0000935769,0.8913007,0.0002734912,0.00142631,0.003282115,0.03605044],"study_design_scores_gemma":[0.000004863987,0.00001772515,0.00602606,0.00001296232,0.000005576137,0.00001064765,0.00002734883,0.9926449,0.00007195746,0.0009021108,0.0002701988,0.000005600193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8132086,0.001887991,0.1695486,0.002427131,0.0001878344,0.0003369125,0.007671299,0.001010779,0.003720857],"genre_scores_gemma":[0.9796261,0.000273218,0.01514884,0.0001272182,0.00005013336,0.0001551861,0.003199765,0.00002282198,0.001396835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1473869,"threshold_uncertainty_score":0.2930581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2364955375522894,"score_gpt":0.3998496966554956,"score_spread":0.1633541591032061,"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."}}