{"id":"W4317936571","doi":"10.1016/j.jval.2023.01.008","title":"Generic Price Regulation and Drug Expenditures: Evidence From Canada","year":2023,"lang":"en","type":"article","venue":"Value in Health","topic":"Pharmaceutical Economics and Policy","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of Nottingham Ningbo China","keywords":"Copayment; Per capita; Incentive; Public economics; Government (linguistics); Prescription drug; Medical prescription; Cost sharing; Price elasticity of demand; Economics; Business; Health care; Health insurance; Economic growth; Microeconomics; Environmental health","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006654669,0.0000877433,0.0002372879,0.0001419757,0.00007251214,0.00003132327,0.0001185904,0.00003486196,0.0001124539],"category_scores_gemma":[0.0001040972,0.00010867,0.00001908662,0.0002043265,0.00001774905,0.0001271377,0.00005091884,0.0001138751,0.0001058393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000379215,"about_ca_system_score_gemma":0.0002259374,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8753782,"about_ca_topic_score_gemma":0.1188453,"domain_scores_codex":[0.9988246,0.00003642928,0.0004968867,0.0003106967,0.00002714136,0.00030427],"domain_scores_gemma":[0.9991887,0.0002850302,0.0001819766,0.0001819913,0.000004080512,0.0001582035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007301958,0.00008875233,0.5003514,0.0006309645,0.00007281327,0.00003217355,0.007745332,0.08355109,0.0000967781,0.2175911,0.1788671,0.01089945],"study_design_scores_gemma":[0.0004732359,0.00002165939,0.5241464,0.0001128294,0.000001234876,0.00000163083,0.00009875835,0.2246113,0.00006080499,0.07000513,0.1801556,0.0003113779],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9744055,0.007908102,0.00004650523,0.01589473,0.0005167926,0.0001840836,0.0001732079,0.00002611849,0.0008449549],"genre_scores_gemma":[0.9915165,0.005221063,0.0001884863,0.002675581,0.0001683466,0.00001513996,0.00002060445,0.00001370727,0.0001805764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7565329,"threshold_uncertainty_score":0.8972335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1379334060196279,"score_gpt":0.3149587185403064,"score_spread":0.1770253125206785,"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."}}