{"id":"W4407573582","doi":"10.1101/2025.02.13.25322207","title":"Real-world brain imaging in a population-based cohort enables accurate markers for dementia","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","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; Itä-Suomen Yliopisto; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Alzheimer's Association","keywords":"Dementia; Cohort; Neuroimaging; Population; Neuroscience; Computer science; Medicine; Psychology; Internal medicine; Environmental health; Disease","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009486967,0.0002660324,0.0002882354,0.00064497,0.0001664767,0.000142849,0.0004202735,0.00009858387,0.00009185752],"category_scores_gemma":[0.001426654,0.0002909284,0.0001502486,0.0005665257,0.00005205742,0.00009535666,0.0001316982,0.0003502466,0.000007792896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002542254,"about_ca_system_score_gemma":0.0002386814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009736326,"about_ca_topic_score_gemma":0.001309523,"domain_scores_codex":[0.9975442,0.0003480292,0.0005649216,0.0009393313,0.0002681598,0.000335402],"domain_scores_gemma":[0.9980744,0.0008833488,0.0003425583,0.0005734995,0.00005873904,0.00006742773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001257398,0.00007745191,0.9711146,0.000314526,0.00001428787,0.000008313827,0.000040424,0.00217238,0.01909087,0.001824828,0.001640618,0.00357601],"study_design_scores_gemma":[0.0005810591,0.000007926998,0.8693299,0.0002346508,0.00004512968,7.09651e-7,0.00001656176,0.1044994,0.01869109,0.002232356,0.004016216,0.0003450007],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9597088,0.00004841361,0.01233566,0.01167711,0.002688646,0.003740962,0.0002334742,0.0005283816,0.009038568],"genre_scores_gemma":[0.9943645,0.00002019155,0.0004552456,0.001534669,0.0000631479,0.0009112991,0.0001070099,0.00002966534,0.002514294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.102327,"threshold_uncertainty_score":0.9999543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03574090228700008,"score_gpt":0.3135585837718733,"score_spread":0.2778176814848733,"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."}}