{"id":"W4391716692","doi":"10.1186/s12874-024-02157-x","title":"Model-based standardization using multiple imputation","year":2024,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; SickKids Foundation; Public Health Ontario; Hospital for Sick Children","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Covariate; Parametric statistics; Computer science; Statistics; Outcome (game theory); Econometrics; Imputation (statistics); Nonparametric statistics; Standardization; Missing data; Data mining; Mathematics; Machine learning","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":[0.04170361,0.001605179,0.003008299,0.003066577,0.0008784294,0.002730202,0.004259877,0.001774277,0.003297551],"category_scores_gemma":[0.09748232,0.001167002,0.00397441,0.005128984,0.001652312,0.002739257,0.003757534,0.003299103,0.001119133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001451854,"about_ca_system_score_gemma":0.004224866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002989851,"about_ca_topic_score_gemma":0.002174309,"domain_scores_codex":[0.96749,0.02383377,0.001478521,0.003004322,0.00370359,0.0004898464],"domain_scores_gemma":[0.9290455,0.04598196,0.005707618,0.01380998,0.005048007,0.0004069002],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003945308,0.0002625865,0.02084658,0.001331855,0.002368946,0.0005129018,0.0007666169,0.458324,0.001868604,0.1487193,0.01379848,0.3508056],"study_design_scores_gemma":[0.0001546596,0.0001205026,0.003283331,0.0002694933,0.0002999345,0.0003239757,0.00006089048,0.7866465,0.00253447,0.1956104,0.01059997,0.0000958479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001897838,0.0001876156,0.9966978,0.0001208348,0.00002917996,0.00009042241,0.0001644369,0.0003562938,0.0004555931],"genre_scores_gemma":[0.1561756,0.0006759206,0.8389982,0.0002467234,0.0001546731,0.0009521479,0.001628618,0.0004401298,0.0007279724],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9582964,"threshold_uncertainty_score":0.2205524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7575667403630337,"score_gpt":0.646695353916137,"score_spread":0.1108713864468968,"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."}}