{"id":"W2121204536","doi":"10.1002/ajim.22329","title":"Using injury severity to improve occupational injury trend estimates","year":2014,"lang":"en","type":"article","venue":"American Journal of Industrial Medicine","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Work & Health; Public Health Ontario; University of Toronto","funders":"National Institute for Occupational Safety and Health; Centers for Disease Control and Prevention; Washington State Department of Labor and Industries; Council of State and Territorial Epidemiologists","keywords":"Medicine; Occupational injury; Occupational safety and health; Injury prevention; Injury Severity Score; Poison control; Incidence (geometry); Workers' compensation; Emergency medicine; Environmental health; Compensation (psychology); Pathology","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":[0.02816034,0.0008245312,0.0008894242,0.005223678,0.0004197265,0.001382989,0.001012101,0.0004578625,0.00154771],"category_scores_gemma":[0.133266,0.0004275888,0.001533323,0.004190484,0.0003629522,0.00169794,0.00150507,0.001078401,0.0003105128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008679074,"about_ca_system_score_gemma":0.002110611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01406676,"about_ca_topic_score_gemma":0.01012577,"domain_scores_codex":[0.984598,0.009604845,0.002450771,0.001368371,0.001602316,0.000375688],"domain_scores_gemma":[0.9291393,0.0383903,0.01495093,0.007565534,0.00940183,0.0005521105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000242842,0.0001100948,0.8624802,0.0004159462,0.00107456,0.00005935154,0.0003936571,0.01742722,0.001378298,0.001695217,0.002852669,0.1118699],"study_design_scores_gemma":[0.0001539439,0.001199933,0.7478297,0.0005959275,0.0009429344,0.0002715822,0.0008013195,0.220031,0.006237622,0.007608358,0.01417722,0.0001503524],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5280972,0.0009593783,0.4561076,0.0009439714,0.0002238099,0.001515896,0.007465203,0.001053385,0.003633556],"genre_scores_gemma":[0.8404855,0.0002955181,0.1515152,0.0001528663,0.00007708712,0.0007177396,0.006273884,0.00009332614,0.0003889965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02816034,"threshold_uncertainty_score":0.1489279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1639095257039681,"score_gpt":0.5176785412884911,"score_spread":0.353769015584523,"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."}}