{"id":"W4401179823","doi":"10.1177/0272989x241264287","title":"Accurate EVSI Estimation for Nonlinear Models Using the Gaussian Approximation Method","year":2024,"lang":"en","type":"article","venue":"Medical Decision Making","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Institute for Clinical Evaluative Sciences; University of Toronto; SickKids Foundation; Hospital for Sick Children; Public Health Ontario","funders":"Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Gaussian; Nonlinear system; Mathematical optimization; Conditional expectation; Computer science; Mathematics; Nonparametric statistics; Spline (mechanical); Conditional probability distribution; Algorithm; Applied mathematics; Econometrics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01262895,0.001225085,0.002027109,0.001984442,0.000478323,0.001309744,0.002084255,0.001516794,0.002576624],"category_scores_gemma":[0.04003858,0.0007495903,0.001823762,0.001878495,0.001304059,0.001983624,0.001626156,0.002513507,0.0006262927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001467153,"about_ca_system_score_gemma":0.002886167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009345352,"about_ca_topic_score_gemma":0.007605556,"domain_scores_codex":[0.9950272,0.002774796,0.0002628542,0.0006596568,0.001034941,0.0002404781],"domain_scores_gemma":[0.9700313,0.02509334,0.001481959,0.001519466,0.001628798,0.0002450737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002920817,0.0001274535,0.00915144,0.0005438774,0.000485068,0.0003045046,0.0003530325,0.7321541,0.002949771,0.08043659,0.002912281,0.1702898],"study_design_scores_gemma":[0.000018907,0.00003935143,0.0009297407,0.00004828409,0.00005316246,0.00006970754,0.00002293268,0.9705961,0.0006346118,0.02631619,0.00124834,0.00002264355],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00397305,0.0002661084,0.9950351,0.00008254637,0.00001600869,0.00003745306,0.0000608916,0.0001593085,0.0003696902],"genre_scores_gemma":[0.2954714,0.001140052,0.69925,0.0003464254,0.0001053438,0.0005178722,0.0006629538,0.0001716858,0.002334218],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01262895,"threshold_uncertainty_score":0.06678909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4975755923639629,"score_gpt":0.5470503735051236,"score_spread":0.04947478114116077,"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."}}