{"id":"W3208943075","doi":"10.1111/biom.13596","title":"Sample size considerations for stepped wedge designs with subclusters","year":2021,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"National Institute on Aging","keywords":"Sample size determination; CRTS; Eigenvalues and eigenvectors; Gaussian; Mathematics; Statistics; Cluster analysis; Sample (material); Computer science; Correlation; Algorithm; Physics; Geometry","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":[0.0003235489,0.0001223748,0.0002409076,0.0002003664,0.0001479602,0.0001094897,0.00006672915,0.00007245605,0.0003755066],"category_scores_gemma":[0.06509157,0.0001000068,0.0000524256,0.001618913,0.00006735903,0.00004581533,0.00003057573,0.00006562457,0.000007117807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004302563,"about_ca_system_score_gemma":0.0001904433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008968068,"about_ca_topic_score_gemma":0.00001491645,"domain_scores_codex":[0.9989898,0.0000925225,0.0002434362,0.0002351118,0.0001965464,0.0002426115],"domain_scores_gemma":[0.9479064,0.05126119,0.00008350122,0.0002527082,0.000385573,0.0001106244],"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":[0.00004179119,0.0002501631,0.0008579111,0.000226796,0.0000860613,0.00003482092,0.0001728085,4.195758e-7,0.00212956,0.9711881,0.0112985,0.01371305],"study_design_scores_gemma":[0.0009863373,0.0001977408,0.0007770024,0.00002593236,0.0001014772,0.00002378938,0.0001500119,0.0005436662,0.005218922,0.9901729,0.001572289,0.0002299724],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001191314,0.00004096363,0.9968507,0.0003914824,0.0001481529,0.0003433599,0.0003727822,0.00005289858,0.0006083795],"genre_scores_gemma":[0.03593042,0.000008248711,0.9634808,0.0002307076,0.00004959567,0.00005148051,0.000006222778,0.00002158569,0.0002209896],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06476802,"threshold_uncertainty_score":0.9427835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2370403399892805,"score_gpt":0.3989707747949734,"score_spread":0.1619304348056929,"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."}}