{"id":"W2104082640","doi":"10.1002/pst.483","title":"Additional results for ‘Sequential design approaches for bioequivalence studies with crossover designs’","year":2011,"lang":"en","type":"article","venue":"Pharmaceutical Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Theratechnologies (Canada)","funders":"","keywords":"Bioequivalence; Crossover; Sample size determination; Statistics; Crossover study; Type I and type II errors; Econometrics; Mathematics; Computer science; Medicine; Pharmacology; Artificial intelligence; Pharmacokinetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1060296,0.002756048,0.002966202,0.004615356,0.00141544,0.001802661,0.002272978,0.003664936,0.06421924],"category_scores_gemma":[0.2563116,0.001230866,0.006664125,0.005842835,0.002293308,0.004404686,0.002162391,0.006972219,0.00974714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002971364,"about_ca_system_score_gemma":0.004850079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002748756,"about_ca_topic_score_gemma":0.001940246,"domain_scores_codex":[0.9185553,0.05694431,0.003364228,0.002802028,0.01713886,0.001195305],"domain_scores_gemma":[0.6730087,0.284691,0.004245928,0.01113959,0.0258203,0.001094462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002071853,0.001341256,0.000879972,0.006080028,0.0007579621,0.0003538131,0.0005579914,0.01042277,0.002452469,0.3265377,0.1617009,0.4868432],"study_design_scores_gemma":[0.002229335,0.003882712,0.00405344,0.002989935,0.0009985289,0.0008330821,0.0001527834,0.04676232,0.01097564,0.5031928,0.4235308,0.0003986343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002792889,0.01309426,0.9052243,0.01327224,0.007549962,0.001778112,0.001844497,0.0009258583,0.05351776],"genre_scores_gemma":[0.05487785,0.01324009,0.8710603,0.01257507,0.005451382,0.006303314,0.002574557,0.001007148,0.03291029],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1060296,"threshold_uncertainty_score":0.5607449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8859019112477156,"score_gpt":0.5762477195149607,"score_spread":0.3096541917327549,"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."}}