{"id":"W4310984321","doi":"10.31235/osf.io/yvsn8","title":"Access to Opioid Agonist Treatment during COVID-19 Public Transport Disruptions","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Opioid Use Disorder Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Office of the Chief Medical Examiner; The Scarborough Hospital; University of Toronto","funders":"","keywords":"Opioid overdose; Pandemic; Public health; Public transport; Medical prescription; Coronavirus disease 2019 (COVID-19); Business; Population; Opioid; Opioid use disorder; Medicine; Medical emergency; Internet privacy; Environmental health; Nursing; Transport engineering; Computer science; Engineering","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":[0.0006620289,0.0002119981,0.0002108712,0.0007976503,0.000582373,0.001506335,0.000556919,0.0004949463,0.003474378],"category_scores_gemma":[0.004931591,0.0001636625,0.0003523146,0.001747778,0.0004279638,0.0005535526,0.0009524935,0.0006020471,0.0005020502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005314632,"about_ca_system_score_gemma":0.004321841,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7437777,"about_ca_topic_score_gemma":0.8040444,"domain_scores_codex":[0.9994811,0.00009260541,0.00002603898,0.00009066628,0.0001418666,0.0001676143],"domain_scores_gemma":[0.9986041,0.0003419175,0.0003745631,0.0001053984,0.0004181256,0.0001559372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003584536,0.0001150311,0.8779386,0.0002656274,0.0002254613,0.0004911245,0.002474265,0.05974985,0.0004394806,0.009001799,0.0267134,0.02222703],"study_design_scores_gemma":[0.00003362332,0.00007886119,0.8748942,0.000241379,0.0000749435,0.0001737066,0.007055534,0.07409629,0.0008274476,0.002989265,0.03945614,0.00007866694],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8974448,0.0003683775,0.00336621,0.001261834,0.00004668874,0.0001238681,0.08237678,0.0001593032,0.01485217],"genre_scores_gemma":[0.95987,0.000308557,0.002172834,0.00009957935,0.00001654442,0.00006566952,0.03349625,0.00003412184,0.003936511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7437777,"threshold_uncertainty_score":0.5154625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07559716768357111,"score_gpt":0.3788979924551084,"score_spread":0.3033008247715372,"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."}}