{"id":"W4390556486","doi":"10.14283/jpad.2023.134","title":"Personalized Computational Causal Modeling of the Alzheimer Disease Biomarker Cascade","year":2024,"lang":"en","type":"article","venue":"The Journal of Prevention of Alzheimer s Disease","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institute of General Medical Sciences; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; BioClinica; Meso Scale Diagnostics; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Canadian Institutes of Health Research; National Science Foundation","keywords":"Biomarker; Dementia; Disease; Personalized medicine; Neuroimaging; Alzheimer's disease; Cognition; Alzheimer's Disease Neuroimaging Initiative; Medicine; Bioinformatics; Psychology; Neuroscience; Internal medicine; Biology","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":[0.001732915,0.0005872779,0.0007677158,0.0006708637,0.0004790851,0.0009724036,0.001133916,0.0009750029,0.002054818],"category_scores_gemma":[0.007050855,0.0006115207,0.001174138,0.0005107409,0.001055906,0.001013229,0.001059187,0.001251392,0.0001266405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00129557,"about_ca_system_score_gemma":0.001424933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01192701,"about_ca_topic_score_gemma":0.00705044,"domain_scores_codex":[0.999428,0.0002812535,0.00002751907,0.0001275891,0.00007477584,0.00006085846],"domain_scores_gemma":[0.9961827,0.002939819,0.0003936388,0.0001783995,0.0001944306,0.0001109455],"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":[0.00003022755,0.00002452865,0.002136832,0.00001742898,0.00002425602,0.00005476614,0.0000472711,0.9862514,0.0002094515,0.00849039,0.000162785,0.00255071],"study_design_scores_gemma":[0.000007548023,0.000007834097,0.0002493771,0.000002236101,0.000006968608,0.00000925841,0.000006894775,0.993609,0.00004981823,0.005916768,0.000131163,0.000003254387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3287489,0.0004258042,0.6623867,0.002015112,0.00008325289,0.0001601725,0.0009241019,0.0003739464,0.004881949],"genre_scores_gemma":[0.9532222,0.0002997008,0.04324772,0.0001743664,0.00004891246,0.0002156981,0.0003543119,0.00003051019,0.002406604],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01192701,"threshold_uncertainty_score":0.0237152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05936578749161757,"score_gpt":0.3670152474254739,"score_spread":0.3076494599338563,"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."}}