{"id":"W2983431646","doi":"10.3389/fncom.2019.00072","title":"Prediction and Classification of Alzheimer’s Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers","year":2019,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Meso Scale Diagnostics; National Research Foundation of Korea; National Research Foundation; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Pfizer; BioClinica; Biogen; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Ministry of Science and ICT, South Korea; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Alzheimer's Association","keywords":"Biomarker; Apolipoprotein E; Dementia; Medicine; Disease; Neuroimaging; Cerebrospinal fluid; Internal medicine; Imaging biomarker; Oncology; Artificial intelligence; Magnetic resonance imaging; Radiology; Computer science; Psychiatry; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001814161,0.0001086029,0.0001473409,0.000280643,0.00007244718,0.00003951142,0.00006924031,0.00001798302,0.0000109957],"category_scores_gemma":[0.00009408898,0.000103196,0.0000267207,0.0002666006,0.0002786508,0.0001441814,0.00003689191,0.0001096086,0.000001533561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003085968,"about_ca_system_score_gemma":0.0001351807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002757087,"about_ca_topic_score_gemma":4.495562e-7,"domain_scores_codex":[0.9986235,0.00007356206,0.0001871468,0.0004152772,0.0005354504,0.0001650215],"domain_scores_gemma":[0.9995092,0.00006196539,0.00007442454,0.0001222916,0.00009229424,0.000139824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009135223,0.0001740976,0.9786372,0.00004202978,0.000009699424,0.00001394803,0.00001834515,0.0007033169,0.014316,0.000117451,0.0005623647,0.004492025],"study_design_scores_gemma":[0.001189953,0.000211569,0.6315002,0.00006220045,0.00002170312,0.000002649982,0.00002543614,0.3660655,0.0002761464,0.0005714112,0.00002340569,0.00004983579],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857592,0.0002684158,0.01130078,0.001323407,0.0003365267,0.0007088689,0.00008908758,0.00002006224,0.0001936696],"genre_scores_gemma":[0.9955057,0.00001724046,0.003809966,0.000495223,0.00001673261,0.00001858263,0.00009754434,0.000008647346,0.00003039242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3653622,"threshold_uncertainty_score":0.4208212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01662354629166961,"score_gpt":0.2745878401972464,"score_spread":0.2579642939055768,"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."}}