{"id":"W4413907297","doi":"10.1002/alz.70094","title":"Bayesian integration of longitudinal and survival outcomes in Alzheimer's disease prediction","year":2025,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Generalizability theory; Alzheimer's Disease Neuroimaging Initiative; Dementia; Clinical Dementia Rating; Multivariate statistics; Neuroimaging; Disease; Population; Psychology; Medicine; Computer science; Machine learning; Internal medicine; Psychiatry; Developmental psychology","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":[0.01907522,0.001272644,0.001585442,0.002211562,0.0005425543,0.001312992,0.001915026,0.001130887,0.00131615],"category_scores_gemma":[0.03077096,0.0006759214,0.002046128,0.001333366,0.000856457,0.001795342,0.00190191,0.002128105,0.0003176671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001303668,"about_ca_system_score_gemma":0.002675401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01658086,"about_ca_topic_score_gemma":0.01586287,"domain_scores_codex":[0.99458,0.003735676,0.0002206761,0.0007144648,0.0005747813,0.0001743832],"domain_scores_gemma":[0.9867375,0.009766581,0.001363943,0.0006960052,0.001036446,0.0003995959],"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":[0.0004749749,0.0002339198,0.05602374,0.0002451403,0.001398049,0.0001658363,0.0002762815,0.7314571,0.001149945,0.02645241,0.002878587,0.179244],"study_design_scores_gemma":[0.0000209738,0.0001140034,0.00423707,0.00005478012,0.0001317082,0.00004240319,0.00001290696,0.9677703,0.0002793063,0.02635223,0.0009500366,0.00003424608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06958347,0.002226595,0.9240018,0.001791751,0.00009709013,0.000110348,0.000739392,0.0004833012,0.000966167],"genre_scores_gemma":[0.7756106,0.001422293,0.2187577,0.0005755232,0.0004747761,0.0003544246,0.001477103,0.0000880716,0.001239554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01907522,"threshold_uncertainty_score":0.1008807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0270194122542528,"score_gpt":0.3287571187042896,"score_spread":0.3017377064500368,"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."}}