{"id":"W3164868662","doi":"10.1136/bmjopen-2020-043964","title":"Safe opioid prescribing: a prognostic machine learning approach to predicting 30-day risk after an opioid dispensation in Alberta, Canada","year":2021,"lang":"en","type":"article","venue":"BMJ Open","topic":"Opioid Use Disorder Treatment","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health; University of Alberta","funders":"","keywords":"Medicine; Logistic regression; Medical prescription; Adverse effect; Percentile; Opioid; Public health; Machine learning; Emergency medicine; Artificial intelligence; Statistics; Internal medicine","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":[0.002129665,0.0006168748,0.0003943051,0.00144346,0.001198567,0.000997007,0.001384612,0.0004172404,0.001077735],"category_scores_gemma":[0.004480804,0.0002942922,0.000476921,0.002015262,0.0005710965,0.0003949834,0.0008374438,0.0007521897,0.0001606965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02256989,"about_ca_system_score_gemma":0.03524436,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9880002,"about_ca_topic_score_gemma":0.9894983,"domain_scores_codex":[0.9992148,0.0001255224,0.0000491207,0.0001224668,0.000298497,0.0001896717],"domain_scores_gemma":[0.9977976,0.0002479758,0.0003421798,0.00006234708,0.001274676,0.0002751998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002345049,0.00009380659,0.9656777,0.00005078708,0.00008012668,0.0001375895,0.0002435117,0.008622974,0.0001734856,0.0003265177,0.002124707,0.02223422],"study_design_scores_gemma":[0.00006016113,0.0001371878,0.8868148,0.0001238559,0.0001171174,0.0001378229,0.001296024,0.1085284,0.0003437379,0.0005279087,0.001876837,0.00003628141],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893141,0.0007024495,0.00319349,0.001378395,0.00002816215,0.0001093676,0.003035906,0.0000779565,0.002160251],"genre_scores_gemma":[0.9939329,0.0003840632,0.00268731,0.0001156583,0.00001786048,0.00002657147,0.001938361,0.000008452851,0.0008888067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02256989,"threshold_uncertainty_score":0.1637568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02796036015339172,"score_gpt":0.3048361137534796,"score_spread":0.2768757536000879,"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."}}