{"id":"W4416012697","doi":"10.1101/2025.11.06.25339696","title":"Substituting Blood-Based Biomarkers for Imaging Measures in Alzheimer’s Disease Studies: Implications for Sample Size and Bias","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Dementia and Cognitive Impairment Research","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; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Sample size determination; Sample (material); Disease; Interpretation (philosophy); Large sample; Biomarker","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2379678,0.00123615,0.001992389,0.0009855203,0.001064299,0.003275052,0.00293619,0.003177961,0.002966931],"category_scores_gemma":[0.4696483,0.000872188,0.003285148,0.001777292,0.004512386,0.003152023,0.002759712,0.003291867,0.0004534733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001824124,"about_ca_system_score_gemma":0.003033591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004606512,"about_ca_topic_score_gemma":0.003148879,"domain_scores_codex":[0.7609615,0.2150786,0.007346008,0.007237945,0.008258291,0.001117713],"domain_scores_gemma":[0.3856875,0.5819152,0.01039629,0.01661557,0.004595498,0.0007898245],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00885164,0.0008832873,0.1902112,0.004856429,0.01118672,0.001764456,0.00279327,0.273716,0.008496339,0.1645569,0.01258115,0.3201025],"study_design_scores_gemma":[0.005290286,0.003914332,0.0560825,0.002955771,0.004135143,0.001537604,0.000631096,0.4771943,0.01208267,0.4012542,0.03451909,0.000402949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1121771,0.00660345,0.8581319,0.01118235,0.001155938,0.003400695,0.001368274,0.0006490581,0.005331237],"genre_scores_gemma":[0.6200979,0.001021777,0.3671049,0.003947693,0.0001855758,0.005947872,0.0005219216,0.0001688113,0.001003631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7620322,"threshold_uncertainty_score":0.9397213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1474295712502176,"score_gpt":0.4175303673684784,"score_spread":0.2701007961182608,"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."}}