{"id":"W6921733427","doi":"10.7939/r3-bh5n-7807","title":"IDENTIFYING ADOLESCENTS AT RISK OF DEVELOPING NEGATIVE OUTCOMES AFTER RECEIVING OPIOID ANALGESICS FOR CHRONIC NON-CANCER PAIN MANAGEMENT USING MACHINE LEARNING ALGORITHMS","year":2023,"lang":"en","type":"dissertation","venue":"University of Alberta Library","topic":"Opioid Use Disorder Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medical prescription; Incidence (geometry); Opioid; Epidemiology; Pharmacy; Opioid epidemic; Pharmacoepidemiology","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":[0.001289517,0.0004512835,0.0005257555,0.001588369,0.0004187278,0.001127026,0.0006907856,0.0006060618,0.001235724],"category_scores_gemma":[0.004892711,0.0002240132,0.0006690214,0.0006626004,0.0001792786,0.0004353574,0.0005182825,0.001066255,0.0002432405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008706009,"about_ca_system_score_gemma":0.001712149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03555709,"about_ca_topic_score_gemma":0.0378898,"domain_scores_codex":[0.999595,0.0001148033,0.00003306408,0.00009471558,0.00006916434,0.00009333104],"domain_scores_gemma":[0.997748,0.00126906,0.0003755727,0.00006580436,0.0003620759,0.0001794588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001929276,0.0004807067,0.922126,0.00004449831,0.0001199842,0.0001430395,0.00008238904,0.02139648,0.0002929601,0.0003391962,0.001640277,0.05314156],"study_design_scores_gemma":[0.00003942506,0.0002552911,0.2705827,0.00007634786,0.0001104658,0.0001626434,0.0003433248,0.7251671,0.0004711892,0.001729085,0.001039874,0.00002265397],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779614,0.0006036671,0.01700339,0.001174563,0.00005094055,0.0001415659,0.001316872,0.000135674,0.00161192],"genre_scores_gemma":[0.9856287,0.0003378449,0.01097889,0.0001184224,0.00004222709,0.00006334475,0.001975013,0.00000741188,0.0008482464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03555709,"threshold_uncertainty_score":0.07070023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383671272400506,"score_gpt":0.2581687123510723,"score_spread":0.2443319996270673,"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."}}