{"id":"W2978303973","doi":"10.1111/jrh.12400","title":"Tracking Opioid Prescribing Metrics in Washington State (2012‐2017): Differences by County‐Level Urban‐Rural and Economic Distress Classifications","year":2019,"lang":"en","type":"article","venue":"The Journal of Rural Health","topic":"Opioid Use Disorder Treatment","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Work & Health","funders":"National Institute for Occupational Safety and Health; Centers for Disease Control and Prevention; Washington State Department of Health; U.S. Department of Health and Human Services","keywords":"Rurality; Metropolitan area; Medicine; Medical prescription; Rural area; Population; Distress; Opioid; Socioeconomic status; Environmental health; Geography; Socioeconomics; Nursing; Sociology","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":[0.0007268711,0.0001693072,0.0004684112,0.0001807132,0.0001183739,0.00005097047,0.0001855811,0.00004480836,0.00002534117],"category_scores_gemma":[0.00003403701,0.0001058932,0.00005897163,0.0001295393,0.0000613032,0.0003230453,0.00003300935,0.0004042412,0.000009446167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005250926,"about_ca_system_score_gemma":0.0003363204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00128883,"about_ca_topic_score_gemma":0.0002099014,"domain_scores_codex":[0.9984823,0.0001535177,0.0006603898,0.00009639546,0.0002694066,0.0003379165],"domain_scores_gemma":[0.9986571,0.0002830496,0.0005818577,0.0002231523,0.00006515824,0.0001896896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006800982,0.0006051032,0.9157964,0.0004915862,0.0001926063,0.000006014209,0.01561093,0.00008939364,0.001193937,0.0002184544,0.008299798,0.0568157],"study_design_scores_gemma":[0.002937375,0.001047634,0.9816695,0.0008461663,0.00009473404,0.0001290817,0.01053369,0.001079231,0.0002103188,0.0002262586,0.001054147,0.0001717857],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824859,0.01199294,0.00008096695,0.004475532,0.0003019778,0.0004476958,0.000137153,0.000009428868,0.00006840811],"genre_scores_gemma":[0.9878603,0.01132665,0.00009660324,0.0001746317,0.00006179401,0.000003163254,0.00002472118,0.0000178828,0.0004343118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0658732,"threshold_uncertainty_score":0.4318198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03933976434095636,"score_gpt":0.3030294296170507,"score_spread":0.2636896652760943,"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."}}