{"id":"W3095936654","doi":"10.3389/fcomp.2020.551481","title":"Latent Class and Transition Analysis of Alzheimer's Disease Data","year":2020,"lang":"en","type":"article","venue":"Frontiers in Computer Science","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; United Arab Emirates University; Eisai; Northern California Institute for Research and Education; 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; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Latent class model; Class (philosophy); Alzheimer's disease; Neuroimaging; Disease; Cognition; Psychology; Medicine; Neuroscience; Internal medicine; Statistics; Computer science; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003340674,0.0000534559,0.0001811278,0.0003060716,0.00003342102,0.00002609068,0.0002221973,0.00001106493,0.00001124436],"category_scores_gemma":[0.00001986263,0.00004640008,0.00002986076,0.001460809,0.0003122641,0.0002194621,0.0001883489,0.00006202303,6.940368e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001199203,"about_ca_system_score_gemma":0.0001062809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007698275,"about_ca_topic_score_gemma":7.014795e-7,"domain_scores_codex":[0.9989238,0.00002594964,0.0001358764,0.0003626426,0.0003986505,0.0001530318],"domain_scores_gemma":[0.9994274,0.000008320109,0.0000256684,0.0002362557,0.00006537219,0.0002369791],"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":[0.0001633254,0.0001494359,0.8686188,0.00004542296,0.0003373587,0.00006800472,0.000618239,0.0004338456,0.0003813829,0.00001946293,0.00138297,0.1277817],"study_design_scores_gemma":[0.0002579813,0.00007381575,0.4567968,0.00001207908,0.0003106645,3.067231e-7,0.00001645074,0.5423204,0.0001201172,0.00001589003,0.00004750193,0.0000280156],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4316786,0.0004698485,0.5635037,0.00399049,0.00009205274,0.0001900222,0.00002647709,0.00001050179,0.0000382883],"genre_scores_gemma":[0.9802186,0.00005735308,0.01892323,0.0007328067,0.00002381491,0.000001878836,0.00003858266,0.000002081663,0.000001645857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5485401,"threshold_uncertainty_score":0.1892141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04567689640616819,"score_gpt":0.3140936782544408,"score_spread":0.2684167818482727,"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."}}