{"id":"W7119915457","doi":"10.11575/prism/50926","title":"Maximizing Value and Reducing Waste: Identifying Suboptimal Prescription Drug Dispensations in Alberta","year":2025,"lang":"en","type":"other","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medical prescription; Formulary; Identification (biology); Biosimilar; Prescription drug; Variation (astronomy); Health information technology; Value (mathematics)","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":[0.006511527,0.0004356457,0.0007076639,0.00353856,0.001090191,0.003435586,0.002072394,0.000754557,0.002179427],"category_scores_gemma":[0.02037472,0.0004547392,0.0007225571,0.005497971,0.001688633,0.0007844234,0.002095964,0.0005465598,0.0001009931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0284546,"about_ca_system_score_gemma":0.0402918,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8549976,"about_ca_topic_score_gemma":0.8615489,"domain_scores_codex":[0.9962577,0.001411073,0.000146688,0.0003963254,0.001080823,0.0007074019],"domain_scores_gemma":[0.9899954,0.005804586,0.001537286,0.0004625523,0.001761621,0.0004385996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008822256,0.0002612961,0.6246492,0.000385105,0.0003594194,0.0006795841,0.002080763,0.2083357,0.001089698,0.03122405,0.00389513,0.1261579],"study_design_scores_gemma":[0.000212617,0.0004248004,0.4257168,0.0002901048,0.0003368504,0.0002492709,0.009077352,0.5213053,0.001778433,0.03067654,0.009796859,0.0001350813],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.9593097,0.000922299,0.02305761,0.001868458,0.00001790978,0.0003681738,0.001707153,0.0001438872,0.01260483],"genre_scores_gemma":[0.9776381,0.0004311246,0.01875818,0.0001756661,0.000007740788,0.00006477253,0.0007187284,0.00001862647,0.002187146],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1450024,"threshold_uncertainty_score":0.2917127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03714888023632096,"score_gpt":0.3174660471726986,"score_spread":0.2803171669363776,"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."}}