{"id":"W2771446961","doi":"10.1136/oemed-2017-104636.49","title":"0068 Using workers compensation data to estimate injury patterns in inter-provincial workers","year":2017,"lang":"en","type":"article","venue":"","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Residence; Cohort; Occupational injury; Census; Workers' compensation; Medicine; Occupational safety and health; Demography; Work (physics); Injury prevention; Workload; Poison control; Environmental health; Compensation (psychology); Psychology; Population; Engineering; Computer science","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":[0.00167629,0.0003215622,0.0002507814,0.003672685,0.0006097453,0.001148335,0.001026631,0.0003591935,0.002090418],"category_scores_gemma":[0.006672767,0.0003531951,0.0006397579,0.005849206,0.00028274,0.0003909162,0.0008436088,0.0003570983,0.0007402008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005125289,"about_ca_system_score_gemma":0.007683164,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9598342,"about_ca_topic_score_gemma":0.9587691,"domain_scores_codex":[0.9983687,0.000195496,0.0001690216,0.0002220552,0.0007370552,0.0003077597],"domain_scores_gemma":[0.9961116,0.0004423703,0.001179019,0.0003403309,0.001545938,0.0003807826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002260942,0.000007409317,0.9967995,0.00001273886,0.00004317057,0.00001044778,0.00006577642,0.0002147087,0.00003162314,0.00002161572,0.0004663864,0.002303924],"study_design_scores_gemma":[0.000004925605,0.00001236193,0.9976057,0.00003218702,0.00001799397,0.0000166883,0.0002849876,0.001189867,0.00003518213,0.00002215735,0.0007738749,0.000004128422],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9668565,0.0005283533,0.001456928,0.0001941704,0.0000230895,0.0001578191,0.02745293,0.0000654722,0.003264711],"genre_scores_gemma":[0.9806846,0.000218855,0.001761641,0.00004793533,0.000006347079,0.00006236178,0.01578939,0.00001232188,0.001416529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04016584,"threshold_uncertainty_score":0.08080471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3240762605568038,"score_gpt":0.5850656956921363,"score_spread":0.2609894351353325,"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."}}