{"id":"W2172241123","doi":"10.1177/0272989x04271040","title":"A Bayesian Approach to Net Health Benefits: An Illustration and Application to Modeling HIV Prevention","year":2004,"lang":"en","type":"article","venue":"Medical Decision Making","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Institute of Mental Health; Agency for Healthcare Research and Quality","keywords":"Bayes' theorem; Quality-adjusted life year; Human immunodeficiency virus (HIV); Bayesian probability; Statistics; Sampling (signal processing); Cost–benefit analysis; Bayesian inference; Econometrics; Monte Carlo method; Medicine; Cost effectiveness; Actuarial science; Computer science; Mathematics; Economics; Filter (signal processing); Family 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":[0.01222325,0.001401565,0.001995169,0.002241868,0.001109972,0.002423354,0.00250112,0.003782954,0.00768087],"category_scores_gemma":[0.03099885,0.001130107,0.001903909,0.00312557,0.002066503,0.002927413,0.001594651,0.003290206,0.0005779725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003079378,"about_ca_system_score_gemma":0.002282701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01566346,"about_ca_topic_score_gemma":0.01576798,"domain_scores_codex":[0.9950967,0.003957175,0.00009587992,0.0002402405,0.0004622634,0.0001477047],"domain_scores_gemma":[0.9798252,0.01877743,0.000440292,0.0002773684,0.0004964619,0.0001833869],"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":[0.00008227975,0.00007818297,0.001093436,0.0001436085,0.0001149186,0.0001721996,0.0001432757,0.5717002,0.0001298525,0.396034,0.00216146,0.02814659],"study_design_scores_gemma":[0.00006470654,0.00005545903,0.0003648875,0.00008171931,0.00005254598,0.0001333798,0.00003237007,0.6445859,0.00005062559,0.3517325,0.002814939,0.0000310268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008310328,0.001906965,0.9773768,0.003221855,0.00008611444,0.0001094495,0.0002067274,0.000110628,0.008671258],"genre_scores_gemma":[0.3914078,0.007453648,0.5863624,0.001182589,0.0004697776,0.001380627,0.0002797924,0.0001328109,0.0113306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01566346,"threshold_uncertainty_score":0.06464344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2855940357967983,"score_gpt":0.4534931842153327,"score_spread":0.1678991484185345,"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."}}