{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01399245,0.0001697338,0.0005909806,0.000410919,0.0002938059,0.0001268538,0.0002721705,0.0001825819,0.00005798633],"category_scores_gemma":[0.002725882,0.0002063369,0.00005055116,0.000349331,0.00001993654,0.0004295712,0.00008501262,0.0001799109,0.0003179506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005581764,"about_ca_system_score_gemma":0.0002135989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002856319,"about_ca_topic_score_gemma":0.000335436,"domain_scores_codex":[0.9955464,0.0001565599,0.002782271,0.0008159874,0.0003170277,0.0003817353],"domain_scores_gemma":[0.9979822,0.0002024702,0.0006020918,0.0004507698,0.00005627603,0.0007061657],"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":[0.00005992622,0.0003449304,0.001137752,0.0001987281,0.00001858088,5.48568e-7,0.004909576,0.4956867,0.000001955746,0.2797066,0.002145647,0.2157891],"study_design_scores_gemma":[0.0007521027,0.0002095468,0.002531647,0.0004729229,0.000002651405,0.00001237879,0.0007539474,0.9185608,5.052084e-7,0.07329093,0.003130985,0.00028157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08586233,0.0003735016,0.8882218,0.02374981,0.0001360599,0.001046439,0.00002920703,0.00006156642,0.0005192967],"genre_scores_gemma":[0.8232185,0.00003915411,0.1596991,0.01644402,0.000325165,0.0001723906,0.00005294826,0.00002814733,0.00002054769],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7373562,"threshold_uncertainty_score":0.8414175,"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."}}