{"id":"W4281559236","doi":"10.5772/intechopen.104694","title":"Practical and Optimal Crossover Designs for Clinical Trials","year":2022,"lang":"en","type":"book-chapter","venue":"IntechOpen eBooks","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Crossover; Sample size determination; Computer science; Statistical power; Crossover study; Adaptive design; Optimal design; Mathematical optimization; Clinical trial; Statistics; Mathematics; Machine learning; Medicine","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","metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.06082018,0.0006611028,0.002557486,0.0004033176,0.0004082201,0.001115812,0.001424995,0.000872317,0.01114876],"category_scores_gemma":[0.04166105,0.0005181285,0.001264888,0.00004156676,0.0009651562,0.000329545,0.001636725,0.001555215,0.0003695929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001663327,"about_ca_system_score_gemma":0.0007669245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001085889,"about_ca_topic_score_gemma":0.000003458882,"domain_scores_codex":[0.9888263,0.002413497,0.004183429,0.00199246,0.002055538,0.0005288292],"domain_scores_gemma":[0.9497732,0.04608081,0.001966684,0.001311133,0.0004382405,0.0004299278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004481847,0.00008445381,0.000006909124,0.0000130797,0.0003893408,0.0001503205,0.000232353,0.000002966225,0.001119117,0.5721174,0.1600933,0.2613089],"study_design_scores_gemma":[0.001434012,0.001144357,0.000002298084,0.00003994432,0.0001650106,0.0001052427,0.0001386934,0.0001950771,0.001187365,0.1247242,0.8702955,0.0005683935],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00002950839,0.0004103701,0.1316979,0.000542829,0.001961856,0.003631411,0.0003581155,0.00009812016,0.8612699],"genre_scores_gemma":[0.0003228075,0.00006138687,0.3307267,0.000944889,0.0004840021,0.0003217925,0.00001255632,0.00013918,0.6669866],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.7102021,"threshold_uncertainty_score":0.9999211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8203676719140467,"score_gpt":0.6616420322207063,"score_spread":0.1587256396933404,"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."}}