Performance of plasma amyloid, tau, and astrocyte biomarkers to identify cerebral AD pathophysiology
Bibliographic record
Abstract
Abstract Introduction Plasma amyloid-β (Aβ), phosphorylated tau (p-tau), and glial fibrillar acid protein (GFAP) can identify Alzheimer’s disease (AD) pathophysiology with high accuracy. However, comparing their performance in the same individuals remains under-explored. Methods We compared the predictive performance of plasma Aβ42/40, p-tau(at threonine 181 and 231), neurofilament light (NfL), and GFAP to identify Aβ- and tau-PET positivity in 138 cognitive unimpaired (CU) and 87 cognitive impaired (CI) individuals. Results In CU, plasma p-tau231 had the best performance to identify both Aβ- and tau-PET positivity. In CI, plasma GFAP showed the best predictive accuracy to identify both Aβ and tau-PET positivity. Discussion Our results support plasma p-tau231 as a marker of early AD pathology and, that GFAP best identifies both PET Aβ and tau abnormalities in the brain of CI individuals. These findings highlight that the performance of blood-based protein biomarkers to identify the presence of AD pathophysiology is disease-stage dependent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".