{"id":"W4380728286","doi":"10.1002/pst.2318","title":"Alone, together: On the benefits of Bayesian borrowing in a meta‐analytic setting","year":2023,"lang":"en","type":"review","venue":"Pharmaceutical Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"College of Veterinarians of British Columbia; NeuroDevNet","funders":"","keywords":"Bayes' theorem; Bayesian probability; Randomized controlled trial; Inference; Sample size determination; Bayesian inference; Econometrics; Fraction (chemistry); Bayesian hierarchical modeling; Randomized response; Meta-analysis; Bayes factor; Statistics; Computer science; Mathematics; Medicine; Artificial intelligence; Internal 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":[0.2075704,0.001691706,0.005387188,0.003644456,0.0006086189,0.004325536,0.003365512,0.003003772,0.005906498],"category_scores_gemma":[0.4353148,0.001478137,0.004451327,0.005386831,0.003356746,0.008102517,0.00501759,0.006306447,0.0006885746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001787271,"about_ca_system_score_gemma":0.003034639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00165998,"about_ca_topic_score_gemma":0.001356968,"domain_scores_codex":[0.7070985,0.2728341,0.006582587,0.003973254,0.009078393,0.0004330743],"domain_scores_gemma":[0.4493711,0.516444,0.01252112,0.0170846,0.003973967,0.0006052129],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00240337,0.000155005,0.003805461,0.01288349,0.01656247,0.0003238715,0.0009754279,0.06199254,0.0005482158,0.3724293,0.00629505,0.5216258],"study_design_scores_gemma":[0.000853898,0.0005865744,0.002402202,0.007700008,0.006549986,0.0003885219,0.000145789,0.09390492,0.0009800451,0.8687106,0.01762991,0.0001475968],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.008463386,0.07018849,0.9008189,0.01115955,0.0004768276,0.0005745927,0.0004577445,0.0003537739,0.007506698],"genre_scores_gemma":[0.3848177,0.03976043,0.562342,0.006398931,0.001421979,0.002270214,0.0003789103,0.0003800894,0.002229731],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.7924296,"threshold_uncertainty_score":0.9772067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8273449729662699,"score_gpt":0.6381815326863675,"score_spread":0.1891634402799024,"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."}}