Plasma phosphorylated tau 217 in relation to Alzheimer’s prevalence and cognition in Thailand
Bibliographic record
Abstract
Abstract Background Biomarkers for Alzheimer's disease (AD) in blood samples has the potential to facilitate early diagnosis and improve the accuracy of AD diagnosis. Plasma phosphorylated tau (p‐tau) is a key biomarker for AD; however, its utility for estimating the prevalence of AD and screening for cognitive impairment is still limited. Method The study recruited 71 participants over the age of 40. Plasma p‐tau 217 levels and neuropsychological data were measured. AD prevalence was estimated using a pre‐defined cut‐off derived from an independent cohort with biomarker‐defined AD status according to the current framework (Jack et. al., 2018). The correlation between plasma p‐tau 217 levels and neuropsychological data were also evaluated. Result The median age of participants was 62 years and overall clinical dementia rating of 0. The median Montreal cognitive assessment (MoCA) and Mini‐Mental State Examination (MMSE) score was 26 and 29 consecutively. Plasma p‐tau 217 levels showed a negative correlation with both neurocognitive tests (MoCA, Rho = ‐0.082, P = 0.5; MMSE, Rho = ‐0.26, P = 0.027). The estimated prevalence of AD was 16.9% (95% CI 9.9‐27.3) when using plasma p‐tau 217 as a biomarker, which was lower than the prevalence estimated using amyloid abnormalities of 30.1% (Jansen et. al., 2022). Conclusion The results of this study suggest the possibility of plasma p‐tau 217 for estimating AD prevalence and cognition. It may represent those at risk of clinical onset. However, further studies are needed to investigate its utility in risk stratification, and cognitive performance assessment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".