{"id":"W4292111498","doi":"10.1002/pds.5527","title":"Meta‐analysis with sample‐standardization in multi‐site studies","year":2022,"lang":"en","type":"review","venue":"Pharmacoepidemiology and Drug Safety","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Standardization; Estimator; Statistics; Meta-analysis; Econometrics; Population; Variance (accounting); Sample (material); Causal inference; Sample size determination; Consistency (knowledge bases); Random effects model; Mathematics; Computer science; Medicine; Artificial intelligence; Economics; Accounting","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007036401,0.0006795014,0.007012663,0.000631433,0.0002242575,0.000007400236,0.0002614548,0.0001980726,0.0005545969],"category_scores_gemma":[0.003149849,0.0004531904,0.0008952306,0.001163004,0.0002722629,0.000152465,0.0003323976,0.001035361,0.000002581706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002552568,"about_ca_system_score_gemma":0.0001006607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008252046,"about_ca_topic_score_gemma":0.0004077833,"domain_scores_codex":[0.9923896,0.004663727,0.001484911,0.0008331506,0.0001775546,0.0004510332],"domain_scores_gemma":[0.9677392,0.0306067,0.001034452,0.0004477873,0.00007810501,0.00009373068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002865236,0.0004009193,0.0008040924,0.02943916,0.5705665,0.0001496081,0.002562259,0.001017067,2.964406e-7,0.04451248,0.002325595,0.3479355],"study_design_scores_gemma":[0.0002614307,0.00002803587,0.000002370771,0.0002577585,0.2654243,0.00001674575,0.00008331068,0.0001228023,7.309366e-7,0.01567821,0.7176183,0.000505989],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[6.418379e-7,0.8160583,0.1822211,0.0001242388,0.00004651997,0.0008233863,0.0004737764,0.0001810599,0.00007103181],"genre_scores_gemma":[0.00001062687,0.9272907,0.07106272,0.0002374919,0.00002751733,0.0008827081,0.0003214614,0.00005354943,0.0001131981],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7152927,"threshold_uncertainty_score":0.999792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6532422003705686,"score_gpt":0.5749269485207112,"score_spread":0.07831525184985733,"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."}}