Performance of plasma biomarkers for diagnosis and prediction of dementia in a Brazilian cohort
Bibliographic record
Abstract
Despite remarkable progress in the biomarker field in recent years, local validation of plasma biomarkers of Alzheimer’s disease (AD) and dementia is still lacking in Latin America. In this longitudinal cohort study of 145 elderly Brazilians, we assess the diagnostic performance of plasma biomarkers, based on clinical diagnosis and CSF biomarker positivity. Follow-up data of up to 4.7 years were used to determine performance in predicting diagnostic conversions. Participants were clinically categorized as cognitively unimpaired (n = 49), amnestic mild cognitive impairment (n = 29), AD (n = 38), Lewy body dementia (n = 22), or vascular dementia (n = 7). Plasma Tau, Aβ40, Aβ42, NfL, GFAP, pTau231, pTau181 and pTau217 were measured on the SIMOA HD-X platform. Plasma pTau217 showed excellent performance determining CSF biomarker status in the cohort, either alone (ROC AUC = 0.94, 95% CI: [0.88–1.00]) or as a ratio to Aβ42 (ROC AUC = 0.98, 95% CI: [0.94–1.00]). This study comprises an initial step towards local validation and adoption of dementia biomarkers in Brazil. Plasma biomarkers of Alzheimer’s disease and dementia are remarkable tools but still lack validation in Low and Middle-Income Countries. In this longitudinal study, authors report on the plasma biomarker profile of a Brazilian dementia cohort.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".