{"id":"W1997947326","doi":"10.1186/1471-2288-11-18","title":"Imputation strategies for missing binary outcomes in cluster randomized trials","year":2011,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hamilton Health Sciences; Population Health Research Institute; McMaster University; St. Joseph’s Healthcare Hamilton","funders":"McMaster University; Canadian Institutes of Health Research; Government of Ontario; Saint Paul University; University of Ottawa","keywords":"Imputation (statistics); Missing data; Randomized controlled trial; MEDLINE; Computer science; Statistics; Medicine; Psychology; Mathematics; Internal medicine; Biology","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":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2422653,0.0001867133,0.00234709,0.0004594263,0.000106607,0.00004456791,0.0004086999,0.0004256048,0.001485922],"category_scores_gemma":[0.8238035,0.0001187274,0.0003073185,0.0002933025,0.000952666,0.0001029765,0.0001533161,0.000642136,0.00001176963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005270838,"about_ca_system_score_gemma":0.001217563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002799918,"about_ca_topic_score_gemma":0.0002064308,"domain_scores_codex":[0.8970419,0.09862755,0.001876101,0.0005097372,0.001076135,0.0008685981],"domain_scores_gemma":[0.3470462,0.6518051,0.0002355236,0.0002846138,0.0003158754,0.0003126736],"domain_codex":null,"domain_gemma":"methods","domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.02613759,0.0001822314,0.0001744958,0.0004392528,0.00006267474,0.00002984374,0.001357022,2.444567e-7,0.0001336612,0.8529637,0.0005392535,0.11798],"study_design_scores_gemma":[0.03572565,0.000221697,0.0004029327,0.0001287522,0.0000419691,0.000007202657,0.0009883861,0.007723248,0.0001817554,0.9544286,0.00002790953,0.0001219175],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005797567,0.00009848039,0.9894934,0.0008862453,0.0003025963,0.001746777,0.000009559282,0.00003938604,0.00162601],"genre_scores_gemma":[0.008350166,0.00004518016,0.9905438,0.0001597624,0.0001255971,0.0006564356,0.000003902131,0.00002540675,0.0000897338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5815383,"threshold_uncertainty_score":0.9994268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8477093270696267,"score_gpt":0.6595906078465309,"score_spread":0.1881187192230958,"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."}}