{"id":"W3035309915","doi":"10.1002/advs.202000675","title":"Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer's Disease","year":2020,"lang":"en","type":"article","venue":"Advanced Science","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Key Research and Development Program of China; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; H. Lundbeck A/S; Servier; General Hospital of People’s Liberation Army; National Natural Science Foundation of China; Eisai; Chinese Academy of Sciences; National Institutes of Health; People’s Liberation Army Navy General Hospital; Northern California Institute for Research and Education; University of Pittsburgh; Pfizer; Biogen; BioClinica; Nvidia; F. Hoffmann-La Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Generalizability theory; Neuroimaging; Dementia; Biomarker; Magnetic resonance imaging; Medicine; Artificial intelligence; Cognitive impairment; Imaging biomarker; Disease; Computer science; Machine learning; Psychology; Pathology; Radiology; Psychiatry; Developmental psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.01214281,0.001109843,0.0009158301,0.002091027,0.000373644,0.002090628,0.001006434,0.001266475,0.001500616],"category_scores_gemma":[0.02082791,0.000314533,0.001019436,0.001267866,0.0007969623,0.001330018,0.001396024,0.001286429,0.0005949101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007049997,"about_ca_system_score_gemma":0.001404152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004101281,"about_ca_topic_score_gemma":0.005194479,"domain_scores_codex":[0.9970901,0.001427685,0.0002776342,0.0006444112,0.0004505985,0.00010945],"domain_scores_gemma":[0.9924697,0.00304518,0.001279873,0.001465608,0.001536411,0.0002032246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001879956,0.0007919522,0.5224939,0.001201241,0.003341224,0.0004801621,0.000486134,0.04888475,0.01999358,0.005721919,0.01778337,0.3769418],"study_design_scores_gemma":[0.0004226594,0.001331926,0.5049214,0.001260998,0.002636305,0.001947802,0.0007124424,0.3472647,0.03494215,0.07690856,0.02735792,0.0002931353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7332342,0.01917096,0.2239509,0.003867064,0.0004871647,0.0005647789,0.01007643,0.001328672,0.007319946],"genre_scores_gemma":[0.9466093,0.001856912,0.04625766,0.0005160885,0.0001815238,0.0002093333,0.003547828,0.00005101306,0.0007704172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01214281,"threshold_uncertainty_score":0.06421804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03038177960294311,"score_gpt":0.3375449546296976,"score_spread":0.3071631750267546,"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."}}