{"id":"W2155839122","doi":"10.1177/0962280214568110","title":"Bayesian regression models for the estimation of net cost of disease using aggregate data","year":2015,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","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 Health Network; Toronto General Hospital; Public Health Ontario; University of Toronto","funders":"","keywords":"Heteroscedasticity; Aggregate (composite); Econometrics; Computer science; Bayesian probability; Skewness; Linear regression; Bayesian linear regression; Linear model; Estimation; Regression; Statistics; Bayesian inference; Data mining; Mathematics; Machine learning; Artificial intelligence; Economics","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1537995,0.0001063533,0.0007386873,0.0003060365,0.0001008705,0.00002645653,0.0008755919,0.0001419727,0.0002693243],"category_scores_gemma":[0.2958139,0.0000895683,0.00003806355,0.0003937541,0.0006614397,0.0002552117,0.0003487704,0.0004105157,0.00001189846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00026322,"about_ca_system_score_gemma":0.00115013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00123419,"about_ca_topic_score_gemma":0.00004824099,"domain_scores_codex":[0.9918088,0.003567459,0.00302395,0.0004993387,0.0006413856,0.0004591001],"domain_scores_gemma":[0.9479891,0.04882473,0.0009077761,0.001204217,0.0003071821,0.0007670036],"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":[0.0004906472,0.0002771342,0.001345544,0.001545854,0.00005169659,0.000005248055,0.0009802827,0.01636476,0.000002785485,0.8117885,0.01520964,0.1519379],"study_design_scores_gemma":[0.0004612796,0.00004445286,0.0003077079,0.00022352,0.000004392395,4.853683e-7,0.0002621223,0.6495389,0.00000433987,0.3480254,0.001074152,0.00005325367],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006460895,0.001767575,0.9830902,0.01159334,0.0002254851,0.001075591,0.001316026,0.000005580832,0.0002800851],"genre_scores_gemma":[0.09559126,0.0001797447,0.9034148,0.0003790518,0.000128127,0.0001186168,0.0001258523,0.00002411961,0.00003845716],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6331742,"threshold_uncertainty_score":0.8713415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8612777946749054,"score_gpt":0.7029062873227251,"score_spread":0.1583715073521803,"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."}}