{"id":"W2031679027","doi":"10.1097/ta.0b013e3181aa093d","title":"Improving Trauma Mortality Prediction Modeling for Blunt Trauma","year":2010,"lang":"en","type":"article","venue":"The Journal of Trauma: Injury, Infection, and Critical Care","topic":"Trauma and Emergency Care Studies","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital de l'Enfant-Jésus","funders":"","keywords":"Glasgow Coma Scale; Logistic regression; Major trauma; Injury Severity Score; Statistic; Medicine; Blunt trauma; Population; Revised Trauma Score; Emergency medicine; Poison control; Statistics; Injury prevention; Medical emergency; Surgery; Internal medicine; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003702452,0.0007657956,0.0006497704,0.0006644302,0.0003336422,0.0007198951,0.0008853164,0.0003060884,0.001432627],"category_scores_gemma":[0.01589755,0.0002192965,0.0005775823,0.0005228965,0.0001480744,0.0005201986,0.0006533487,0.0006241106,0.000302582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001021861,"about_ca_system_score_gemma":0.002571307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03573969,"about_ca_topic_score_gemma":0.03323932,"domain_scores_codex":[0.9989671,0.0006299086,0.00005888629,0.0001371209,0.0001421631,0.00006485508],"domain_scores_gemma":[0.9948919,0.00329937,0.0004566039,0.0003401772,0.0008736635,0.0001383131],"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":[0.0006931284,0.000294774,0.2857526,0.0001249929,0.0004649318,0.0002112546,0.0002062584,0.5185684,0.002089147,0.001705646,0.004646444,0.1852424],"study_design_scores_gemma":[0.00001937127,0.0001110354,0.01518014,0.00001721235,0.00004908241,0.00004151424,0.00002654313,0.9825696,0.0004988899,0.0008858698,0.0005883487,0.00001251928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6727624,0.0006234166,0.3202932,0.001401237,0.0001125466,0.0002999838,0.001232083,0.001410625,0.001864543],"genre_scores_gemma":[0.9132113,0.0002291704,0.08416017,0.00009218598,0.00006989476,0.0001295568,0.001000459,0.00006149842,0.001045742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03573969,"threshold_uncertainty_score":0.07106334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03341621438835683,"score_gpt":0.3438238962022501,"score_spread":0.3104076818138932,"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."}}