{"id":"W2951733170","doi":"10.1101/274324","title":"Reproducible evaluation of classification methods in Alzheimer’s disease: framework and application to MRI and PET data","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Horizon 2020 Framework Programme; Genentech; National Institutes of Health; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; National Science Foundation; Assistance publique-Hôpitaux de Paris; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; Agence Nationale de la Recherche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Foundation for the National Institutes of Health; Campus France; European Commission; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Computer science; Preprocessor; Artificial intelligence; Feature extraction; Pattern recognition (psychology); Feature (linguistics); Smoothing; Set (abstract data type); Data mining; Benchmarking; Modular design; Machine learning; Computer vision","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":[0.08297095,0.003129526,0.001684117,0.004386291,0.001697208,0.003758189,0.005330056,0.003312981,0.002430134],"category_scores_gemma":[0.09989546,0.0007742465,0.002590866,0.003051253,0.002574804,0.002610321,0.006557775,0.003056833,0.00193408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002541897,"about_ca_system_score_gemma":0.004018268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008443383,"about_ca_topic_score_gemma":0.007362998,"domain_scores_codex":[0.9423256,0.03002262,0.005231149,0.00897682,0.01197117,0.001472558],"domain_scores_gemma":[0.9293466,0.02452837,0.005216307,0.01932072,0.01963508,0.001952908],"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":[0.009122698,0.00778837,0.07757503,0.004844878,0.004492,0.00104832,0.001763748,0.1042968,0.07071204,0.00658816,0.06424183,0.6475261],"study_design_scores_gemma":[0.003650221,0.0108432,0.1268004,0.001601251,0.001387937,0.001977734,0.001638247,0.6086636,0.1849321,0.01841208,0.0393848,0.0007084473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4161219,0.008920224,0.5178227,0.002050045,0.001731532,0.008042943,0.01136348,0.02777699,0.006170112],"genre_scores_gemma":[0.5839065,0.0007733416,0.3808692,0.0006908776,0.0002720135,0.005452025,0.02340354,0.002271362,0.002361145],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08297095,"threshold_uncertainty_score":0.4387975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09852545105022983,"score_gpt":0.4166100229687843,"score_spread":0.3180845719185544,"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."}}