Alzheimer's Disease Plasma Biomarkers Performance in Brazilian Individuals
Bibliographic record
Abstract
Alzheimer's disease (AD) biomarker research has largely concentrated on populations from the Global North. The emergence of blood-based biomarkers presents an opportunity to reduce this disparity. In this perspective presentation, I will present data on the performance of blood-based biomarkers in a real-world, memory clinic-based cohort from Brazil, a population characterized by lower educational attainment compared to those typically studied in the Global North. Specifically, I will examine the performance of plasma biomarkers-Aβ40, Aβ42, p-tau217, NfL, and GFAP-in differentiating AD from cognitively unimpaired (CU) individuals and vascular dementia (VaD) in a Brazilian cohort (n = 59). Preliminary findings indicate that p-tau217 exhibits the highest accuracy in distinguishing AD from CU (AUC 0.96). However, the performance of all plasma biomarkers in differentiating AD from VaD is lower (AUC 0.52 to 0.79) than expected based on studies conducted in the Global North. Finally, I will share initial findings from the Brazilian Initiative of Blood Biomarkers in Neurodegenerative Disorders, a program funded by the Ministry of Health. Additionally, I will discuss the role of blood biomarkers in shaping state and national dementia plans in Brazil.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".