{"id":"W2994209430","doi":"10.1007/s40271-019-00402-w","title":"A Hierarchical Bayes Approach to Modeling Heterogeneity in Discrete Choice Experiments: An Application to Public Preferences for Prenatal Screening","year":2019,"lang":"en","type":"article","venue":"Patient","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Centre for Advancing Health Outcomes; St. Paul's Hospital","funders":"St. Paul's Foundation","keywords":"Concordance; Population; Bayes' theorem; Miscarriage; Prenatal screening; Medicine; Sample (material); Prenatal diagnosis; Psychology; Actuarial science; Pregnancy; Statistics; Environmental health; Bayesian probability; Economics","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":[0.07846112,0.002066167,0.006459418,0.003012636,0.002545725,0.004675607,0.005567453,0.005586979,0.01077385],"category_scores_gemma":[0.1338076,0.002722997,0.005314103,0.003765609,0.003939488,0.005463592,0.003055152,0.007697147,0.0008172141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004390604,"about_ca_system_score_gemma":0.004448703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02317765,"about_ca_topic_score_gemma":0.02492953,"domain_scores_codex":[0.9526622,0.03982981,0.00102104,0.003105558,0.002216657,0.001164604],"domain_scores_gemma":[0.6673766,0.318837,0.004515028,0.005708104,0.002120505,0.001442733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00138867,0.0006880724,0.01208364,0.0005869398,0.001967928,0.001011504,0.002206899,0.3733467,0.0008855286,0.5239642,0.003728005,0.07814186],"study_design_scores_gemma":[0.0003569595,0.0001539455,0.001338855,0.00005916068,0.0002558857,0.0001689299,0.0001383864,0.7384819,0.0001373191,0.2579751,0.0008608851,0.00007258479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.026737,0.000610995,0.9688853,0.001318842,0.0001034038,0.0003880344,0.000363813,0.0002891448,0.001303374],"genre_scores_gemma":[0.3991149,0.001215107,0.5885116,0.001037247,0.0004132115,0.002099097,0.0005680237,0.0001834828,0.006857371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07846112,"threshold_uncertainty_score":0.414947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05943707312412069,"score_gpt":0.3256547414379966,"score_spread":0.2662176683138759,"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."}}