{"id":"W1633467982","doi":"10.1002/sim.4326","title":"An informed reference prior for between‐study heterogeneity in meta‐analyses of binary outcomes","year":2011,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; St. Joseph’s Healthcare Hamilton","funders":"","keywords":"Prior probability; Variance (accounting); Vagueness; Bayesian probability; Computer science; Inference; Bayesian inference; Econometrics; Meta-analysis; Statistics; Mathematics; Artificial intelligence; Fuzzy logic; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1954604,0.00234855,0.005057243,0.007432294,0.001496038,0.006346666,0.007225385,0.01053694,0.004670464],"category_scores_gemma":[0.5114664,0.002957118,0.006401758,0.00689217,0.004175882,0.009209388,0.004160282,0.01255106,0.001724695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003179513,"about_ca_system_score_gemma":0.002658322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002067662,"about_ca_topic_score_gemma":0.00219718,"domain_scores_codex":[0.8325071,0.1363906,0.008552004,0.008919804,0.01264821,0.0009823217],"domain_scores_gemma":[0.6332871,0.3055543,0.01561861,0.033237,0.01144107,0.0008619576],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001347192,0.000228254,0.004467637,0.003908802,0.004141429,0.0009918406,0.002490006,0.1801725,0.003151993,0.580556,0.0135291,0.2050153],"study_design_scores_gemma":[0.0006019367,0.0004535102,0.004521951,0.002573121,0.003017458,0.0007713152,0.0001745354,0.1471094,0.003046188,0.8179579,0.01945708,0.0003156895],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0024205,0.002194404,0.9913958,0.001539142,0.0001994632,0.0002575156,0.0003588079,0.0003396493,0.001294731],"genre_scores_gemma":[0.1474755,0.003266991,0.839496,0.002503072,0.0006140044,0.003232288,0.001655441,0.0003018733,0.001454911],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8045396,"threshold_uncertainty_score":0.9921406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5384232542332547,"score_gpt":0.4078123679400847,"score_spread":0.1306108862931699,"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."}}