{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1167788,0.00437944,0.01102021,0.01295652,0.00150655,0.004115906,0.005034355,0.003196224,0.01303603],"category_scores_gemma":[0.1999955,0.002270579,0.0504327,0.008947679,0.001402917,0.002984904,0.004513397,0.005783566,0.0008549091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002859464,"about_ca_system_score_gemma":0.005541224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008249261,"about_ca_topic_score_gemma":0.01061455,"domain_scores_codex":[0.8368361,0.1353327,0.009518701,0.01152435,0.005899685,0.0008884491],"domain_scores_gemma":[0.8637633,0.1106848,0.005514625,0.01755355,0.001936703,0.0005470723],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003137329,0.0001067646,0.0149223,0.03505615,0.8174943,0.0007410076,0.000238968,0.03225928,0.001774247,0.01900424,0.01420941,0.06105614],"study_design_scores_gemma":[0.004986858,0.001955785,0.01646704,0.005285667,0.6765323,0.0007987093,0.0002325474,0.1253375,0.002285434,0.1223192,0.04340958,0.00038942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01338543,0.07528408,0.8758079,0.004310427,0.002663061,0.008204777,0.01419693,0.003559777,0.002587561],"genre_scores_gemma":[0.2569418,0.01712023,0.6924037,0.002250172,0.0006164713,0.02268859,0.005206915,0.0006255604,0.002146594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8832212,"threshold_uncertainty_score":0.6175927,"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."}}