{"id":"W1992166682","doi":"10.1007/s10926-015-9582-5","title":"A New Method to Classify Injury Severity by Diagnosis: Validation Using Workers’ Compensation and Trauma Registry Data","year":2015,"lang":"en","type":"article","venue":"Journal of Occupational Rehabilitation","topic":"Trauma and Emergency Care Studies","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Ontario; University of Toronto; Institute for Work & Health","funders":"National Institute for Occupational Safety and Health","keywords":"Medicine; Injury Severity Score; Workers' compensation; Injury prevention; Concordance; Poison control; Occupational safety and health; Medical record; Akaike information criterion; Major trauma; Rehabilitation; Cohen's kappa; Traumatic injury; Abbreviated Injury Scale; Emergency medicine; Physical therapy; Medical emergency; Compensation (psychology); Statistics; Surgery; Internal medicine; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001337029,0.0001249769,0.000288211,0.0001884876,0.00007203387,0.00003043983,0.000102239,0.00008139846,0.00002524651],"category_scores_gemma":[0.002637111,0.000112149,0.00006161952,0.0002997817,0.00003585472,0.0006014179,0.00004865296,0.0001611769,0.000002807441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00026068,"about_ca_system_score_gemma":0.0004454646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001848198,"about_ca_topic_score_gemma":0.00001823873,"domain_scores_codex":[0.9982243,0.0001767525,0.0006165224,0.0002207376,0.0006466934,0.000115041],"domain_scores_gemma":[0.997886,0.0004090579,0.0003600121,0.0002263161,0.0008434212,0.0002751884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002659485,0.0001993813,0.6617894,0.00009639572,0.0001307541,0.000002998841,0.003122163,0.0003593601,0.002383587,0.0001076524,0.1908872,0.1382616],"study_design_scores_gemma":[0.001427887,0.001035396,0.9783347,0.0002654831,0.000229621,0.00006303074,0.002933434,0.0007959331,0.0005254295,0.0008626218,0.01337707,0.0001493874],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.901117,0.0003776354,0.09323233,0.004490347,0.0003310835,0.0002728147,0.00006091916,0.00001243269,0.0001053846],"genre_scores_gemma":[0.7189861,0.0000137165,0.2804594,0.0001257964,0.0002768901,0.000004689812,0.00008942321,0.000010294,0.00003365576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3165453,"threshold_uncertainty_score":0.4573305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1638363248471021,"score_gpt":0.4562672098427286,"score_spread":0.2924308849956264,"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."}}