{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0183649,0.0005751443,0.0009655337,0.003650089,0.00088335,0.001477735,0.001154781,0.000831001,0.002196065],"category_scores_gemma":[0.03400885,0.0003023029,0.001893002,0.00284761,0.001005632,0.001485618,0.00173344,0.002047298,0.0005522696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028082,"about_ca_system_score_gemma":0.001097285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009099468,"about_ca_topic_score_gemma":0.005341842,"domain_scores_codex":[0.9876493,0.008857359,0.0005203421,0.001485577,0.0007790318,0.0007083782],"domain_scores_gemma":[0.9724547,0.01936041,0.00258934,0.00384028,0.001097472,0.0006578591],"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":[0.002422209,0.0008993753,0.8474282,0.0001357801,0.0008457201,0.0003259627,0.001820963,0.04130527,0.001156826,0.01220099,0.00464931,0.08680937],"study_design_scores_gemma":[0.0001491395,0.0005614234,0.3518957,0.00007762259,0.0001643557,0.0003562928,0.001217738,0.6144567,0.00096151,0.02675115,0.003280916,0.00012742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8438111,0.0004323924,0.1457428,0.000539555,0.00009814616,0.0003992649,0.00743366,0.0005512972,0.0009918009],"genre_scores_gemma":[0.9765716,0.00006074198,0.01634162,0.00003770605,0.00003120619,0.0002838579,0.006215614,0.00003978487,0.0004178919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0183649,"threshold_uncertainty_score":0.09712404,"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."}}