{"id":"W2040016992","doi":"10.1016/j.neuroimage.2012.10.086","title":"Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials","year":2012,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":123,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute on Aging; National Institutes of Health; Genentech; U.S. National Library of Medicine; IXICO; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, Los Angeles; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; Alzheimer's Association; Amorfix Life Sciences; National Center for Research Resources; F. Hoffmann-La Roche; Medpace; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb; Bayer HealthCare; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Synarc","keywords":"Neuroimaging; Alzheimer's Disease Neuroimaging Initiative; Sample size determination; Statistical power; Magnetic resonance imaging; Atrophy; Robustness (evolution); Clinical trial; Psychology; Medicine; Alzheimer's disease; Disease; Neuroscience; Internal medicine; Statistics; Radiology; Mathematics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1916095,0.001459926,0.004084344,0.001982082,0.0008863613,0.002748161,0.001932986,0.003580988,0.00242438],"category_scores_gemma":[0.3519371,0.001556058,0.002314048,0.001790351,0.002227975,0.003452639,0.002744163,0.003076276,0.0004634667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007728055,"about_ca_system_score_gemma":0.001938883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009404311,"about_ca_topic_score_gemma":0.001172802,"domain_scores_codex":[0.8832833,0.1070888,0.003256878,0.003013948,0.003021266,0.0003359047],"domain_scores_gemma":[0.743037,0.2135407,0.01541445,0.02047003,0.006571094,0.00096676],"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":[0.09123205,0.001149136,0.04492232,0.005527275,0.02233821,0.0006993919,0.001706024,0.07680267,0.01786443,0.04483798,0.01253064,0.6803899],"study_design_scores_gemma":[0.03808353,0.01485734,0.05690271,0.001230899,0.01834949,0.001834748,0.0002901224,0.6062512,0.01811511,0.222193,0.02125086,0.0006409191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1343777,0.01651712,0.8352323,0.004056909,0.001065666,0.003024757,0.000820802,0.001590567,0.003314177],"genre_scores_gemma":[0.6211124,0.002024434,0.3697353,0.001076037,0.0004428,0.003768574,0.0004809277,0.0003959204,0.0009636839],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1916095,"threshold_uncertainty_score":0.9968894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3249834224463085,"score_gpt":0.4847802800648447,"score_spread":0.1597968576185363,"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."}}