{"id":"W1986129723","doi":"10.1016/j.annemergmed.2007.09.007","title":"Using Information on Preexisting Conditions to Predict Mortality From Traumatic Injury","year":2008,"lang":"en","type":"article","venue":"Annals of Emergency Medicine","topic":"Trauma and Emergency Care Studies","field":"Medicine","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; McGill University; Hôpital Charles-Le Moyne; Montreal General Hospital; Hôpital de l'Enfant-Jésus","funders":"","keywords":"Receiver operating characteristic; Medicine; Area under the curve; Baseline (sea); Comorbidity; Risk assessment; Risk of mortality; Emergency medicine; Internal medicine; Surgery; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001324961,0.0005049524,0.0004775178,0.001738952,0.0005821133,0.0008168322,0.0004221399,0.0006453567,0.002165277],"category_scores_gemma":[0.007467101,0.0002263482,0.001084182,0.001492521,0.0003735206,0.0009975691,0.0005839257,0.00107383,0.000262656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004555175,"about_ca_system_score_gemma":0.0008176074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007049535,"about_ca_topic_score_gemma":0.01458379,"domain_scores_codex":[0.9993873,0.0002352717,0.00009251267,0.00006753935,0.00009787663,0.0001193881],"domain_scores_gemma":[0.9950504,0.002117112,0.001052155,0.0003591703,0.0004435321,0.0009776806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001274,0.00004459202,0.9989518,0.000005744223,0.00005165425,0.0000188819,0.00001530593,0.00003382252,0.00003870928,0.000009506131,0.00004230291,0.0006601496],"study_design_scores_gemma":[0.00001296223,0.0002758112,0.9986033,0.00001045751,0.0001076251,0.0001243855,0.00009604103,0.0005339795,0.00007264416,0.00005046303,0.0001078175,0.000004473717],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981815,0.0002135762,0.0001166593,0.0001076387,0.00002425257,0.00001115577,0.0005021861,0.000003823195,0.0008392101],"genre_scores_gemma":[0.9990018,0.0001032184,0.0001345771,0.00002580991,0.00002987664,0.000005811062,0.0005599027,0.00000157851,0.0001374832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007049535,"threshold_uncertainty_score":0.01401699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4259591011402241,"score_gpt":0.4758081145596234,"score_spread":0.04984901341939929,"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."}}