{"id":"W3026466697","doi":"10.1016/j.jval.2020.04.743","title":"PMS57 EPIDEMIOLOGIC MODELING TO GUIDE PAYER DECISIONS IN RARE DISEASES: A CASE STUDY OF DUCHENNE MUSCULAR DYSTROPHY IN THE UNITED STATES","year":2020,"lang":"en","type":"article","venue":"Value in Health","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vancouver Coastal Health","funders":"","keywords":"Population; Duchenne muscular dystrophy; Medicine; Epidemiology; Incidence (geometry); Sample size determination; Estimation; Demography; Pediatrics; Statistics; Environmental health; Mathematics; Pathology","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":[0.005262317,0.0003469373,0.0004292066,0.001025647,0.0007877271,0.001626384,0.0007565712,0.001442997,0.006163805],"category_scores_gemma":[0.02184946,0.000362242,0.0008858459,0.001394287,0.0003745583,0.0007465883,0.0007687426,0.001252836,0.0003752398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00174797,"about_ca_system_score_gemma":0.003048546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04908661,"about_ca_topic_score_gemma":0.04561327,"domain_scores_codex":[0.997876,0.001670783,0.00007443911,0.0001182977,0.0001252652,0.0001351363],"domain_scores_gemma":[0.985858,0.0123662,0.000537207,0.0003752846,0.0006010615,0.0002623024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003996521,0.001091714,0.4157085,0.0001398486,0.0003503461,0.003579519,0.0008854762,0.4217767,0.0003352003,0.04013069,0.02572477,0.08987751],"study_design_scores_gemma":[0.0001312065,0.0001549807,0.04165999,0.000158838,0.0001323448,0.0008364305,0.001559469,0.9206017,0.0002840619,0.02528216,0.009158169,0.00004062378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8878644,0.001098435,0.06154342,0.02276199,0.0001385471,0.0002822773,0.003604048,0.0002287796,0.02247811],"genre_scores_gemma":[0.981566,0.0004636533,0.01339084,0.000388774,0.00005169026,0.00007547999,0.0007704012,0.00004014302,0.003253058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04908661,"threshold_uncertainty_score":0.09760177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6033858586141566,"score_gpt":0.4710968884644692,"score_spread":0.1322889701496874,"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."}}