{"id":"W2068618230","doi":"10.1016/s0378-3758(99)00185-8","title":"Crossover designs for two-treatment clinical trials","year":2000,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Crossover; Crossover study; Mathematics; Repeated measures design; Clinical trial; Statistics; Sample size determination; Design of experiments; Optimal design; Sequential analysis; Treatment and control groups; Clinical study design; Computer science; Medicine; Machine learning","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01626038,0.0001522756,0.0009337456,0.00009720597,0.0001334544,0.0003706447,0.0002532061,0.00008214239,0.001263815],"category_scores_gemma":[0.03169401,0.00008874269,0.0001856388,0.0001172166,0.0002451774,0.0002889296,0.00002044468,0.0001888658,0.00003649892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003430561,"about_ca_system_score_gemma":0.0001593636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000728087,"about_ca_topic_score_gemma":2.931773e-7,"domain_scores_codex":[0.9953322,0.001412546,0.002147434,0.0002650507,0.0006109321,0.0002318346],"domain_scores_gemma":[0.9520695,0.04653335,0.0006316298,0.0001565398,0.0002747634,0.0003342648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003218303,0.000278601,0.009905938,0.000004371524,0.0001024114,0.00009292165,0.0005152105,0.0008180674,0.001333603,0.008902488,0.00903574,0.9657924],"study_design_scores_gemma":[0.02799615,0.03354097,0.1129515,0.0005198724,0.0006630694,0.0006768259,0.00182569,0.08983385,0.00597553,0.600633,0.1239445,0.001439092],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08902163,0.0005379848,0.9076267,0.0001387687,0.0003579361,0.0001970104,0.0001070912,0.000007419201,0.002005399],"genre_scores_gemma":[0.6173777,0.00005684201,0.3814595,0.0001864719,0.000255707,0.000004551997,0.000001598672,0.000007717722,0.0006499065],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9643533,"threshold_uncertainty_score":0.9996492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7098386298084299,"score_gpt":0.672377921310636,"score_spread":0.03746070849779393,"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."}}