{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004986032,0.000415685,0.0008823578,0.0004049817,0.0002453407,0.0002304208,0.0007425792,0.0003350664,0.001025963],"category_scores_gemma":[0.0004692965,0.0002325487,0.001214169,0.0008852188,0.0008919373,0.0001883274,0.0002359233,0.0002883642,0.00006519356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000259297,"about_ca_system_score_gemma":0.0001468012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003423993,"about_ca_topic_score_gemma":0.00001579136,"domain_scores_codex":[0.9976438,0.0003993466,0.0006352937,0.0004957424,0.0001724503,0.0006533124],"domain_scores_gemma":[0.9981441,0.0005054626,0.0002428997,0.0008840989,0.0001235239,0.00009986327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.005120355,0.003723535,0.1749156,0.005433001,0.1402685,0.0001158846,0.006440987,0.000475927,0.05513322,0.06670586,0.2792353,0.2624319],"study_design_scores_gemma":[0.003530058,0.0009763754,0.7709129,0.002084957,0.0155214,0.00009101396,0.001717718,0.0009397198,0.1075349,0.03560739,0.05892611,0.002157509],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8391201,0.09315505,0.0005677268,0.0490569,0.002517044,0.001247274,0.01141846,0.0003763369,0.00254113],"genre_scores_gemma":[0.9930458,0.000924952,0.00004043396,0.003439431,0.00009318627,0.00003186726,0.00004845702,0.00003119481,0.002344657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5959972,"threshold_uncertainty_score":0.9998872,"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."}}