{"id":"W2147040082","doi":"10.1080/10543400500406553","title":"Quasi-Empirical Bayes Methodology for Improving Meta-Analysis","year":2006,"lang":"en","type":"article","venue":"Journal of Biopharmaceutical Statistics","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Carleton University","funders":"University of Alberta","keywords":"Meta-analysis; Bayes' theorem; Computer science; Outlier; Odds; Statistics; Odds ratio; Econometrics; Mathematics; Bayesian probability; Medicine; Logistic regression","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1357546,0.0003896537,0.008632855,0.0009471412,0.0001678301,0.0006712404,0.001543726,0.0001612737,0.01250138],"category_scores_gemma":[0.05804729,0.0001771967,0.009434908,0.00201278,0.0001691315,0.000185528,0.0001072902,0.0003894086,0.0001941211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005989738,"about_ca_system_score_gemma":0.0001359709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001529597,"about_ca_topic_score_gemma":0.0000268899,"domain_scores_codex":[0.9677913,0.01197257,0.0143608,0.0006851761,0.004723376,0.0004667695],"domain_scores_gemma":[0.9471778,0.03867842,0.00888598,0.001147383,0.003734581,0.0003758679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.0005271173,0.002203473,0.01557624,0.0003822028,0.3183616,0.0003209916,0.000332948,0.00428823,0.003737227,0.1070532,0.4808416,0.06637516],"study_design_scores_gemma":[0.0008084721,0.0005284614,0.002329274,0.000001700799,0.5770063,0.0001202996,0.0001842961,0.1126691,0.0007044002,0.1123672,0.1928507,0.0004298909],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002008049,0.003290826,0.990845,0.002431743,0.000364455,0.0003523736,0.0004633412,0.000003227439,0.00024097],"genre_scores_gemma":[0.2329516,0.00001742939,0.7633436,0.000924899,0.0003976015,0.00001466489,0.00001307223,0.00001903882,0.002318066],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2879909,"threshold_uncertainty_score":0.9884014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9277533342042015,"score_gpt":0.6544762144967344,"score_spread":0.273277119707467,"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."}}