{"id":"W2752127454","doi":"10.1002/sim.7440","title":"Algorithms for evaluating reference scaled average bioequivalence: power, bias, and consumer risk","year":2017,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Bioequivalence; Computer science; Statistics; Econometrics; Sample size determination; Statistical power; Mathematics; 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"],"consensus_categories":[],"category_scores_codex":[0.01338169,0.0002888713,0.001042401,0.0001090214,0.0004243693,0.00006922319,0.0004820748,0.0002038315,0.0004873788],"category_scores_gemma":[0.5732478,0.0002255301,0.00003658815,0.00007690348,0.001444577,0.00006657434,0.000196583,0.000557314,0.000009823902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005200998,"about_ca_system_score_gemma":0.00008058419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001483025,"about_ca_topic_score_gemma":0.00004969743,"domain_scores_codex":[0.996129,0.0008100418,0.001333465,0.0006074937,0.0006495515,0.0004704175],"domain_scores_gemma":[0.9087291,0.08885118,0.0009716239,0.0008841884,0.0003563584,0.0002075484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005269368,0.0002110524,0.01474319,0.0009279922,0.0001713895,0.00009431558,0.0008818047,0.000002047448,0.000341872,0.5531629,0.01455452,0.414382],"study_design_scores_gemma":[0.004779784,0.0007759284,0.01140659,0.0005519334,0.0002362967,0.000005752645,0.0001229663,0.00913182,0.00005475599,0.9722644,0.0004032659,0.000266506],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01316781,0.0002688239,0.9791681,0.0004929342,0.001308004,0.00124927,0.002393476,0.00004860527,0.001902984],"genre_scores_gemma":[0.05349584,0.000836232,0.9448524,0.0001238385,0.0002245394,0.00008921942,0.00001190984,0.000043214,0.0003228466],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5598662,"threshold_uncertainty_score":0.9196851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7233084269583646,"score_gpt":0.6410520296377792,"score_spread":0.08225639732058543,"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."}}