P-tau217 as a Biomarker in Alzheimer’s Disease: Applications in Latin American Populations
Bibliographic record
Abstract
Alzheimer's disease (AD) is one of the primary dementia causes worldwide. For this reason, there is a need for plasma-based diagnostic biomarkers to facilitate the timely diagnosis of AD. This work synthesizes the current evidence concerning the tau protein p-tau phosphorylated at threonine 217 (p-tau217) as an emerging biomarker, emphasizing its utility in preclinical phases and its potential application in Latin American populations. The findings indicate that p-tau217 has superior sensitivity and specificity compared to classical biomarkers such as p-tau181 and Aβ42. Likewise, its plasma concentration regulates neuropathological progression, as studies by Braak have shown, enabling it to identify alterations from the early stages. In Latin America, studies in Peru, Colombia, and Brazil have shown promising results, albeit with methodological limitations. Some of them have small sample sizes or lack neuroimaging confirmation. Additionally, clinical factors common in the region, such as hypertension, diabetes, or chronic kidney disease, may alter the clinical interpretation. In short, p-tau217 represents a potential non-invasive diagnostic resource. More diverse cohorts are needed to confirm its validity in daily clinical practice.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".