{"id":"W7066205105","doi":"","title":"IMPROVING PRECISION BY ADJUSTING FOR BASELINE VARIABLES IN RANDOMIZED TRIALS WITH BINARY OUTCOMES, WITHOUT REGRESSION MODEL ASSUMPTIONS","year":2016,"lang":"en","type":"article","venue":"Collection of Biostatistics Research Archive","topic":"Atomic and Molecular Physics","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Food and Drug Administration; Hamilton Health Sciences Foundation","keywords":"Covariate; Baseline (sea); Sample size determination; Regression; Regression analysis; Randomized controlled trial; Binary number; Sample (material); Binary data","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5389328,0.003153539,0.007324685,0.006947192,0.001706344,0.006782604,0.005458749,0.006730033,0.007502116],"category_scores_gemma":[0.8206995,0.003225826,0.01051928,0.0122941,0.005347193,0.00666061,0.005903419,0.01003651,0.002803433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002910384,"about_ca_system_score_gemma":0.008808065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003140977,"about_ca_topic_score_gemma":0.003064445,"domain_scores_codex":[0.3441895,0.5786317,0.03555624,0.01917686,0.02081154,0.001634166],"domain_scores_gemma":[0.2028287,0.6424546,0.04450689,0.09822277,0.01109487,0.0008921154],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.008177296,0.0004457877,0.03062542,0.0200586,0.02345843,0.0006027927,0.004310752,0.04896123,0.002246398,0.139945,0.0582485,0.6629198],"study_design_scores_gemma":[0.01130752,0.002447069,0.02410934,0.009864652,0.01476828,0.0008750025,0.0003934257,0.08883017,0.00827851,0.7262198,0.1120541,0.0008520774],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004448938,0.008488293,0.9637646,0.008079291,0.001847127,0.003885783,0.002074295,0.002771772,0.004639859],"genre_scores_gemma":[0.1247407,0.002920312,0.8448231,0.007097458,0.001400677,0.01468841,0.001663866,0.0009718323,0.001693698],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4610672,"threshold_uncertainty_score":0.568578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06071523194507317,"score_gpt":0.3847473783563576,"score_spread":0.3240321464112845,"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."}}