{"id":"W4399481889","doi":"10.1136/annrheumdis-2024-eular.1852","title":"POS0149 DO POLICIES THAT IMPROVE BIOSIMILAR UPTAKE ALSO HELP ACHIEVE GREATER EXPENDITURE CONTROL? A CROSS-COUNTRY POLICY ANALYSIS","year":2024,"lang":"en","type":"article","venue":"Annals of the Rheumatic Diseases","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Calgary; University of British Columbia; Centre for Advancing Health Outcomes","funders":"","keywords":"Biosimilar; Control (management); Business; Computer science; Medicine; Artificial intelligence; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.01756081,0.0007126752,0.001068965,0.001919387,0.0007930438,0.005403047,0.00129989,0.003083296,0.02036463],"category_scores_gemma":[0.03541338,0.0005305047,0.002446835,0.004301366,0.001942557,0.003533181,0.002790546,0.004083019,0.001075827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006528187,"about_ca_system_score_gemma":0.009959971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04056624,"about_ca_topic_score_gemma":0.02207529,"domain_scores_codex":[0.9810415,0.01093104,0.0005242899,0.0006672379,0.001900247,0.004935805],"domain_scores_gemma":[0.9579602,0.02731807,0.006575345,0.001203125,0.004761445,0.002181661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02922045,0.00375292,0.3966934,0.006075263,0.01602381,0.001558661,0.001714492,0.07695045,0.002902453,0.2620966,0.05907298,0.1439386],"study_design_scores_gemma":[0.004410627,0.00521253,0.7683305,0.003961292,0.01207653,0.000478291,0.0187803,0.02155334,0.006721784,0.03761232,0.1205101,0.0003524263],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7157879,0.01412774,0.008310303,0.1278496,0.001338191,0.0008789692,0.01451409,0.000195038,0.1169981],"genre_scores_gemma":[0.9755171,0.002365732,0.001212455,0.01309371,0.0001467528,0.0002721623,0.001976172,0.00003830642,0.005377674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04056624,"threshold_uncertainty_score":0.09287155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0304162211447627,"score_gpt":0.3427143278562319,"score_spread":0.3122981067114692,"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."}}