ArticleResearch square2026
Bloodwork-free Early Screening for Alzheimer's Disease via Comorbid Pattern Recognition in Electronic Health Records.
Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Authors and funding
3 authors.
Funding
Abstract
Early identification of Alzheimer's disease and related dementias (ADRD) remains limited by the need for specialized tests and late-stage diagnosis. The Zero-burden Risk Assessment (ZeBRA) is a AI-driven score that predicts incident ADRD up to a decade before diagnosis, using only routine electronic health record (EHR) data, without laboratory tests, imaging, or questionnaires. Trained on 487,989 cases and 12,483,718 controls from nationwide U.S. insurance claims and validated on held-back samples, and two independent cohorts, ZeBRA achieved AUC = 0.93 and 0.83 for predicting out to 1-year and 10-year horizons respectively, maintaining positive likelihood ratios (>10) at 95% specificity and stable discrimination over time (AUC drop ≈ 1 to 1.3% per year). Performance was consistent across age, sex, race, and ethnicity subgroups. In a limited prospective pilot, higher ZeBRA scores correlated with lower Montreal Cognitive Assessment (MoCA) scores, indicating a greater degree of cognitive impairment (
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Registered trials
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