{"id":"W2569952854","doi":"10.1177/0962280216684671","title":"Detecting and correcting for publication bias in meta-analysis – A truncated normal distribution approach","year":2016,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Publication bias; Funnel plot; Estimator; Statistics; Selection bias; Meta-analysis; Random effects model; Parametric statistics; Truncation (statistics); Econometrics; Computer science; Mathematics; Confidence interval; 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":[0.2529451,0.002361894,0.006708583,0.009349948,0.001171417,0.005849988,0.007639614,0.00540468,0.003701256],"category_scores_gemma":[0.5154334,0.001651244,0.009916366,0.008869125,0.005272824,0.007589507,0.004641498,0.006344784,0.0006387231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0033461,"about_ca_system_score_gemma":0.009301124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002322022,"about_ca_topic_score_gemma":0.001569802,"domain_scores_codex":[0.7450805,0.2031409,0.0184767,0.01138819,0.02085496,0.001058697],"domain_scores_gemma":[0.5051792,0.4361629,0.01989978,0.02624972,0.01182376,0.0006846173],"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":[0.001389673,0.000194599,0.01073619,0.01362865,0.02020117,0.001552967,0.00168226,0.103357,0.002439159,0.1831681,0.007522088,0.6541281],"study_design_scores_gemma":[0.00141273,0.0007851806,0.003781463,0.00410119,0.00949728,0.00146593,0.0002432832,0.3049846,0.004839178,0.6545139,0.01401466,0.0003606852],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001621747,0.004455864,0.9915946,0.0009023272,0.0002244055,0.0003431717,0.0001431263,0.0003022709,0.0004124337],"genre_scores_gemma":[0.1302449,0.008228942,0.8543236,0.001597828,0.0006114271,0.003194219,0.0004637774,0.000276922,0.001058394],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7470549,"threshold_uncertainty_score":0.9212517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5688831534431038,"score_gpt":0.6084837730401228,"score_spread":0.03960061959701899,"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."}}