{"id":"W2888918636","doi":"10.1016/j.dadm.2018.07.008","title":"Bayesian latent time joint mixed‐effects model of progression in the Alzheimer's Disease Neuroimaging Initiative","year":2018,"lang":"en","type":"article","venue":"Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Eisai; Pfizer; Novartis Pharmaceuticals Corporation; Weston Brain Institute; F. Hoffmann-La Roche; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Neuroimaging; Dementia; Positron emission tomography; Magnetic resonance imaging; Alzheimer's Disease Neuroimaging Initiative; Disease; Psychology; Cognitive decline; Cognition; Concordance; Medicine; Internal medicine; Neuroscience; Radiology","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":[0.01811668,0.001158767,0.002416212,0.002361149,0.0008343434,0.002780589,0.00320881,0.002672068,0.006742052],"category_scores_gemma":[0.03229147,0.001126838,0.003106323,0.00198905,0.001873245,0.002645069,0.001922845,0.002938586,0.0009289728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002301205,"about_ca_system_score_gemma":0.002277695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0253862,"about_ca_topic_score_gemma":0.02004612,"domain_scores_codex":[0.9932448,0.004330004,0.0002194353,0.001389515,0.000343283,0.0004730229],"domain_scores_gemma":[0.9775504,0.01756868,0.00215769,0.00112206,0.001074306,0.0005268177],"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":[0.001758585,0.0006467214,0.1193948,0.0004073239,0.002269035,0.0006793527,0.001251416,0.6114304,0.001265995,0.2075964,0.007327629,0.04597238],"study_design_scores_gemma":[0.0002920112,0.0002635685,0.01573659,0.0001188053,0.0004104009,0.0001694173,0.0001178055,0.8931186,0.0002564024,0.08680514,0.002604011,0.0001073335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3340413,0.001685297,0.6456335,0.005316784,0.000301592,0.0005019978,0.008410349,0.0007992372,0.003310009],"genre_scores_gemma":[0.9169793,0.000677601,0.06590556,0.0004027769,0.0001855588,0.001420349,0.005057331,0.0001060527,0.009265379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0253862,"threshold_uncertainty_score":0.09581131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0547388682992179,"score_gpt":0.3644779007627603,"score_spread":0.3097390324635424,"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."}}