{"id":"W3082112708","doi":"10.1002/sta4.462","title":"A hierarchical meta‐analysis for settings involving multiple outcomes across multiple cohorts","year":2022,"lang":"en","type":"preprint","venue":"Stat","topic":"Prenatal Substance Exposure Effects","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Foundation for the National Institutes of Health","keywords":"Confounding; Random effects model; Cohort; Propensity score matching; Psychology; Cognition; Cohort study; Multilevel model; Medicine; Demography; Meta-analysis; Clinical psychology; Statistics; Internal medicine; Mathematics; Psychiatry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001602839,0.0007288367,0.003254803,0.0003572285,0.0003638434,0.0001130563,0.0005176679,0.0003796705,0.0004312429],"category_scores_gemma":[0.003010241,0.0006227484,0.00344238,0.0005641015,0.0001627144,0.0001029478,0.001765735,0.001711073,0.00001109712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004081581,"about_ca_system_score_gemma":0.0002295447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004822381,"about_ca_topic_score_gemma":0.001219519,"domain_scores_codex":[0.9953516,0.0002926957,0.0008619237,0.001474219,0.001007643,0.001011882],"domain_scores_gemma":[0.9939823,0.003421949,0.0004895012,0.001497534,0.0002126161,0.0003960965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.00105167,0.0004782341,0.7136039,0.002586102,0.2662209,0.0004020994,0.00597122,0.004940445,0.001469894,0.00004833097,0.002430799,0.0007964188],"study_design_scores_gemma":[0.01517268,0.001048954,0.3792473,0.0002942056,0.4774045,0.00004899852,0.002323829,0.07859265,0.00722295,0.00315702,0.03147954,0.004007384],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9558919,0.01027045,0.01482314,0.002642198,0.001454622,0.006870373,0.007142143,0.0007345782,0.0001706032],"genre_scores_gemma":[0.9696159,0.00006391193,0.02100545,0.001107093,0.0001756696,0.003052139,0.003608717,0.0001453965,0.001225737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3343566,"threshold_uncertainty_score":0.9996224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05359399379942213,"score_gpt":0.3461517645884846,"score_spread":0.2925577707890624,"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."}}