{"id":"W4415389914","doi":"10.1016/j.neuroimage.2025.121548","title":"Aging as an active player in Alzheimer’s disease classification: Insights from feature selection in BrainAge models","year":2025,"lang":"en","type":"article","venue":"NeuroImage","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; National Institute on Aging; Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona; Genentech; IXICO; Servier; Eisai; U.S. Department of Defense; Eli Lilly and Company; Eusko Jaurlaritza; H. Lundbeck A/S; Ikerbasque, Basque Foundation for Science; Ministerio de Sanidad, Consumo y Bienestar Social; Ministerio de Ciencia e Innovación; Northern California Institute for Research and Education; DoD Alzheimer's Disease Neuroimaging Initiative; BioClinica; Biogen; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Feature selection; Disease; Selection (genetic algorithm); Feature (linguistics); Computational model","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":[0.004037128,0.001177749,0.0007975522,0.001031641,0.0003269132,0.001158181,0.0008044185,0.000882154,0.001535523],"category_scores_gemma":[0.009789578,0.0002336459,0.001060586,0.000561182,0.0006249109,0.001295535,0.0010508,0.001250108,0.0005417025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000468061,"about_ca_system_score_gemma":0.0007156418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003130074,"about_ca_topic_score_gemma":0.003082313,"domain_scores_codex":[0.9993336,0.0002566639,0.0000416218,0.0001813017,0.0001057307,0.00008113651],"domain_scores_gemma":[0.9965073,0.002324104,0.0002861617,0.000290308,0.0004329129,0.0001591482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001006388,0.0004870298,0.1367896,0.0003252646,0.0007155899,0.0008406703,0.0006612604,0.3259859,0.01256387,0.02542308,0.01513618,0.4800651],"study_design_scores_gemma":[0.00003000352,0.000181392,0.01737273,0.00006783349,0.0001175744,0.0003099911,0.00005826143,0.9432644,0.002306383,0.03269158,0.003562483,0.000037357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2944241,0.003390088,0.6928793,0.002966079,0.0002623427,0.0001693596,0.0013586,0.001442262,0.003107949],"genre_scores_gemma":[0.928839,0.0007366418,0.06464787,0.0004776219,0.0003006784,0.0001570367,0.001558121,0.0001254343,0.003157588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004037128,"threshold_uncertainty_score":0.02135062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04026074635599153,"score_gpt":0.3511928401182285,"score_spread":0.310932093762237,"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."}}