{"id":"W2127117802","doi":"10.1016/j.neuroimage.2015.07.087","title":"Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge","year":2015,"lang":"en","type":"article","venue":"NeuroImage","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"FP7 Information and Communication Technologies; National Institute on Aging; Engineering and Physical Sciences Research Council; Medical Research Council; Canadian Institutes of Health Research; Alzheimer's Society; Agence Nationale de la Recherche; Brain Research Trust; Wolfson Foundation; National Institute for Health and Care Research; Multiple Sclerosis Society of Canada; University College London Hospitals NHS Foundation Trust; Alzheimer's Disease Neuroimaging Initiative; Multiple Sclerosis Society; University of Pennsylvania; National Institutes of Health; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Institut national de recherche en informatique et en automatique (INRIA); GlaxoSmithKline","keywords":"Atrophy; Dementia; Repeatability; Cerebral atrophy; Time point; Psychology; Computer science; Statistics; Medicine; Pathology; Disease; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.09093991,0.002476116,0.002099151,0.002952297,0.001450846,0.002763767,0.002809736,0.002600107,0.00116008],"category_scores_gemma":[0.1701934,0.0007046581,0.003265007,0.002104162,0.001534678,0.002601715,0.007461685,0.002455805,0.001027859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001327318,"about_ca_system_score_gemma":0.001450029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003551068,"about_ca_topic_score_gemma":0.005193464,"domain_scores_codex":[0.9309312,0.04438822,0.004666443,0.008017195,0.01108656,0.0009103786],"domain_scores_gemma":[0.8135308,0.1224933,0.01149028,0.02248557,0.02632274,0.003677371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01787249,0.002523924,0.3798637,0.007023424,0.01409745,0.0007799879,0.006189553,0.02822411,0.01220594,0.007171192,0.06213804,0.4619102],"study_design_scores_gemma":[0.002161062,0.01161405,0.765998,0.002197699,0.004087808,0.004397445,0.003189268,0.08005113,0.02204185,0.01731732,0.08589561,0.001048832],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7504115,0.03263941,0.164713,0.007161302,0.002117543,0.003296223,0.02497757,0.004066429,0.01061697],"genre_scores_gemma":[0.7611277,0.002141184,0.1980156,0.002069645,0.0006892855,0.003003769,0.02784516,0.001779674,0.003327982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09093991,"threshold_uncertainty_score":0.480942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1560921768461273,"score_gpt":0.3588195066786921,"score_spread":0.2027273298325648,"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."}}