{"id":"W4323569680","doi":"10.51731/cjht.2022.591","title":"Automatic Stop Orders for Opioids","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Health Technologies","topic":"Opioid Use Disorder Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Observational study; Medical prescription; Opioid; Context (archaeology); Medicine; Adverse effect; Pharmacology; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005900448,0.0004250078,0.0007314918,0.001516532,0.001313835,0.002262658,0.00204385,0.001209165,0.03408919],"category_scores_gemma":[0.0453371,0.0003766996,0.002091819,0.001616187,0.0009311554,0.001654839,0.00127372,0.002458464,0.0030864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004731788,"about_ca_system_score_gemma":0.01487758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05282661,"about_ca_topic_score_gemma":0.08674068,"domain_scores_codex":[0.9887179,0.00348642,0.001759355,0.000587362,0.004532685,0.0009162637],"domain_scores_gemma":[0.9572281,0.01864002,0.01043675,0.003539857,0.00815014,0.002005112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003218798,0.001111169,0.03431249,0.01352812,0.0009510891,0.0005569249,0.0009861131,0.0007074674,0.001213783,0.01375981,0.1344107,0.7952436],"study_design_scores_gemma":[0.003022342,0.003192106,0.2001875,0.02255375,0.001821154,0.001966524,0.00154751,0.001870822,0.005553168,0.009633393,0.7483695,0.0002822682],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2449714,0.1581183,0.02703576,0.07121926,0.01089775,0.006129387,0.03144435,0.004668098,0.4455157],"genre_scores_gemma":[0.8128536,0.05340094,0.03373799,0.03566242,0.003500173,0.002111611,0.01207752,0.0005688446,0.04608682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05282661,"threshold_uncertainty_score":0.1140397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03600269691823518,"score_gpt":0.328037259506079,"score_spread":0.2920345625878439,"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."}}