{"id":"W4386317080","doi":"10.1097/ccm.0000000000006030","title":"Predicting ICU Mortality in Acute Respiratory Distress Syndrome Patients Using Machine Learning: The Predicting Outcome and STratifiCation of severity in ARDS (POSTCARDS) Study*","year":2023,"lang":"en","type":"article","venue":"Critical Care Medicine","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"Instituto de Salud Carlos III; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Medicine; ARDS; Logistic regression; Receiver operating characteristic; Mechanical ventilation; Intensive care medicine; APACHE II; Emergency medicine; Internal medicine; Intensive care unit; Lung","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":[0.008128533,0.001273192,0.0009049764,0.0007051844,0.0002747924,0.000935811,0.000734238,0.0008594184,0.0005455888],"category_scores_gemma":[0.01106403,0.0004406991,0.000967179,0.000425793,0.0005298162,0.0008000508,0.000883377,0.001097632,0.0002022397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005743224,"about_ca_system_score_gemma":0.001177098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002496745,"about_ca_topic_score_gemma":0.002038741,"domain_scores_codex":[0.998231,0.001259064,0.00007669812,0.0002088384,0.0001485341,0.00007586907],"domain_scores_gemma":[0.9940853,0.003032991,0.0008799604,0.0007744294,0.0005276172,0.000699626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007090231,0.001714175,0.9731704,0.00007971712,0.0009174346,0.00009680596,0.00009265205,0.003840046,0.0006811369,0.00007196006,0.001065889,0.01117959],"study_design_scores_gemma":[0.004149411,0.009525009,0.9259843,0.00004759109,0.000807702,0.000215095,0.0001564609,0.05744695,0.0007553588,0.0002146655,0.000655508,0.00004195507],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983078,0.0001013521,0.000792615,0.000138769,0.00001817537,0.0001152245,0.0003943144,0.00001339614,0.0001183466],"genre_scores_gemma":[0.9953922,0.0001346117,0.00208629,0.0001459654,0.0001069449,0.0001905433,0.001765753,0.00001165363,0.0001660635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008128533,"threshold_uncertainty_score":0.04298836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1323663808361304,"score_gpt":0.4230836825010509,"score_spread":0.2907173016649206,"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."}}