{"id":"W2542885621","doi":"10.1016/j.jval.2016.09.2202","title":"Use of Net Monetary Benefit Analysis to Comprehensively Understand the Value of Innovative Treatments","year":2016,"lang":"en","type":"article","venue":"Value in Health","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bristol-Myers Squibb (Canada)","funders":"","keywords":"Nivolumab; Value (mathematics); Willingness to pay; Productivity; Cost–benefit analysis; Actuarial science; Medicine; Incremental cost-effectiveness ratio; Quality-adjusted life year; Cost effectiveness; Economics; Risk analysis (engineering); Cancer; Internal medicine; Statistics; Mathematics; Microeconomics; Immunotherapy","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":[0.005977936,0.0001692413,0.001275889,0.0008633807,0.00008253615,0.00001346388,0.0002336988,0.0000794518,0.00008914504],"category_scores_gemma":[0.001084125,0.000139039,0.0001241576,0.001311526,0.00009995805,0.0002330965,0.00005532341,0.00008127585,0.00007472994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007518808,"about_ca_system_score_gemma":0.000190455,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03883781,"about_ca_topic_score_gemma":0.0005516897,"domain_scores_codex":[0.9945489,0.0004385269,0.004103696,0.0004289513,0.0001268513,0.0003531058],"domain_scores_gemma":[0.9941018,0.002500557,0.002504628,0.0006183499,0.0001378477,0.0001368195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007998647,0.0002017049,0.5469273,0.0002531082,0.0009720314,6.004821e-7,0.006197424,0.06982034,0.00001290198,0.3729455,0.001562668,0.00102637],"study_design_scores_gemma":[0.001026541,0.0003519367,0.9629951,0.0002520204,0.00002792074,8.485481e-7,0.001085492,0.01089533,0.00002735199,0.02060644,0.002498935,0.0002321085],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9533974,0.001106127,0.005177951,0.03788538,0.0001526471,0.0009871585,0.001096597,0.00001147461,0.0001852738],"genre_scores_gemma":[0.9911051,0.0004174184,0.003570462,0.004643121,0.00003478537,0.00002464214,0.00001663077,0.00001919769,0.0001686146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4160677,"threshold_uncertainty_score":0.9675627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5343160658507669,"score_gpt":0.4181268274163777,"score_spread":0.1161892384343892,"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."}}