{"id":"W2570375830","doi":"10.1371/journal.pone.0168011","title":"Early Prediction of Alzheimer’s Disease Using Null Longitudinal Model-Based Classifiers","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Secretaría de Estado de Investigación, Desarrollo e Innovación; Instituto de Salud Carlos III; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; Genentech; IXICO; Bristol-Myers Squibb; Ministerio de Economía y Competitividad; Generalitat de Catalunya; Northern California Institute for Research and Education; DoD Alzheimer's Disease Neuroimaging Initiative; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Null (SQL); Disease; Null model; Alzheimer's disease; Artificial intelligence; Biology; Medicine; Bioinformatics; Computer science; Internal medicine; Data mining; Ecology","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":[0.01377622,0.001026921,0.0015254,0.001424144,0.0003775211,0.001527121,0.00180865,0.001140628,0.001250817],"category_scores_gemma":[0.01732343,0.0004368553,0.001423238,0.0004607148,0.0005843777,0.001202464,0.001025299,0.001634596,0.0005004311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007511406,"about_ca_system_score_gemma":0.0007857026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005352824,"about_ca_topic_score_gemma":0.00306214,"domain_scores_codex":[0.998207,0.0009122621,0.000154634,0.0003922035,0.0001417431,0.0001923078],"domain_scores_gemma":[0.9825597,0.01341537,0.001145791,0.0009189034,0.001408792,0.0005514583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004998962,0.0008796544,0.2430764,0.0002081263,0.00106128,0.0005959714,0.0003413662,0.617674,0.003732808,0.0037865,0.00247693,0.121168],"study_design_scores_gemma":[0.00001064175,0.0001283511,0.004367973,0.00001146831,0.00003238952,0.00003711069,0.00001929597,0.9939373,0.0002850097,0.001079022,0.0000806676,0.000010752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7520499,0.001554453,0.2433224,0.0005631958,0.0001534653,0.00007483937,0.0008423228,0.000760081,0.0006792807],"genre_scores_gemma":[0.9853045,0.0001604959,0.01271128,0.00006488602,0.000039822,0.00003961092,0.001110423,0.00003711645,0.0005318505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01377622,"threshold_uncertainty_score":0.07285655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2094738488557916,"score_gpt":0.3448765569676461,"score_spread":0.1354027081118545,"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."}}