{"id":"W1986034915","doi":"10.1371/journal.pone.0025635","title":"Bayesian Hierarchical Models Combining Different Study Types and Adjusting for Covariate Imbalances: A Simulation Study to Assess Model Performance","year":2011,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; St. Joseph’s Healthcare Hamilton; Programs for Assessment of Technology in Health Research Institute","funders":"Social Sciences and Humanities Research Council of Canada; Ontario Ministry of Health and Long-Term Care","keywords":"Covariate; Bayesian probability; Bayesian hierarchical modeling; Statistics; Clinical study design; Medicine; Bayesian inference; Bayes' theorem; Computer science; Econometrics; Clinical trial; Mathematics; Internal medicine","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2146181,0.002788119,0.005137434,0.004121291,0.0014551,0.004473178,0.00447068,0.00546579,0.007243893],"category_scores_gemma":[0.4204734,0.002048392,0.01592818,0.004292555,0.002280651,0.004798409,0.004357313,0.006132777,0.0006833242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005364286,"about_ca_system_score_gemma":0.005667747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01353759,"about_ca_topic_score_gemma":0.008797136,"domain_scores_codex":[0.8108324,0.1731822,0.005680759,0.004667402,0.004180155,0.001457012],"domain_scores_gemma":[0.366273,0.595835,0.01872179,0.01132814,0.006756054,0.001086101],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01320968,0.0006746713,0.02935078,0.006805081,0.02633129,0.0008199823,0.001631885,0.7965415,0.0007041415,0.06319272,0.002995413,0.05774292],"study_design_scores_gemma":[0.007047026,0.002613073,0.00538487,0.002784633,0.01641519,0.0004819972,0.0003107104,0.8623747,0.000744715,0.09752603,0.003973719,0.0003434297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1747413,0.011648,0.7866693,0.003473366,0.0004931776,0.01223547,0.004153407,0.0007985496,0.005787414],"genre_scores_gemma":[0.6705092,0.002374273,0.3101833,0.001070339,0.0001149942,0.01274052,0.001599619,0.0001232705,0.001284391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7853819,"threshold_uncertainty_score":0.9685157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8854818235961188,"score_gpt":0.4904874303079804,"score_spread":0.3949943932881385,"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."}}