{"id":"W4410101089","doi":"10.1177/13872877251337944","title":"Modeling Alzheimer’s disease: Bayesian copula graphical model from demographic, cognitive, and neuroimaging data","year":2025,"lang":"en","type":"article","venue":"Journal of Alzheimer s Disease","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":0,"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; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Eisai; National Institute on Aging; Alzheimer's Association","keywords":"Neuroimaging; Bayesian probability; Confounding; Partial correlation; Cognition; Partial volume; Graphical model; Posterior cingulate; Correlation; Orbitofrontal cortex; Psychology; Neuroscience; Computer science; Artificial intelligence; Mathematics; Statistics; Prefrontal cortex","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":[0.00558133,0.001369387,0.001741555,0.001733757,0.0005333903,0.001744915,0.002597112,0.002184684,0.002828239],"category_scores_gemma":[0.01512212,0.001087163,0.002366752,0.001783901,0.001513446,0.001652632,0.001627785,0.0024831,0.0006851951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002121734,"about_ca_system_score_gemma":0.00218616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0383805,"about_ca_topic_score_gemma":0.0246382,"domain_scores_codex":[0.9977048,0.001402749,0.00006823261,0.0004672084,0.0001767582,0.0001802778],"domain_scores_gemma":[0.9923081,0.006102405,0.000608601,0.0002613167,0.0005226356,0.0001969202],"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":[0.0001164458,0.00007458205,0.005761051,0.00009818812,0.0002338833,0.0001681645,0.000199279,0.9409777,0.0004275776,0.03524824,0.002288096,0.01440681],"study_design_scores_gemma":[0.00002805221,0.00002496344,0.0009672847,0.00001808508,0.00004326825,0.00004311456,0.00001721313,0.9719782,0.00006305389,0.02608869,0.0007107644,0.00001721576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06387909,0.00124721,0.9275249,0.001710472,0.00007772082,0.0002167297,0.002472891,0.0007394453,0.002131477],"genre_scores_gemma":[0.7202501,0.002207622,0.2637716,0.0007826418,0.0001963903,0.001299622,0.004346611,0.0002874559,0.006858027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0383805,"threshold_uncertainty_score":0.07631421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06434383918065163,"score_gpt":0.3580210468156557,"score_spread":0.293677207635004,"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."}}