Plasma pTau‐217 and N‐terminal tau (NTA) enhance sensitivity to identify tau PET positivity in amyloid‐β positive individuals
Bibliographic record
Abstract
Abstract INTRODUCTION We set out to identify tau PET‐positive (A+T+) individuals among amyloid‐beta (Aβ) positive participants using plasma biomarkers. METHODS In this cross‐sectional study we assessed 234 participants across the AD continuum who were evaluated by amyloid PET with [18F]AZD4694 and tau‐PET with [18F]MK6240 and measured plasma levels of total tau, pTau‐181, pTau‐217, pTau‐231, and N‐terminal tau (NTA‐tau). We evaluated the performances of plasma biomarkers to predict tau positivity in Aβ+ individuals. RESULTS Highest associations with tau positivity in Aβ+ individuals were found for plasma pTau‐217 (AUC [CI95%] = 0.89 [0.82, 0.96]) and NTA‐tau (AUC [CI95%] = 0.88 [0.91, 0.95]). Combining pTau‐217 and NTA‐tau resulted in the strongest agreement (Cohen's Kappa = 0.74, CI95% = 0.57/0.90, sensitivity = 92%, specificity = 81%) with PET for classifying tau positivity. DISCUSSION The potential for identifying tau accumulation in later Braak stages will be useful for patient stratification and prognostication in treatment trials and in clinical practice. Highlights We found that in a cohort without pre‐selection pTau‐181, pTau‐217, and NTA‐tau showed the highest association with tau PET positivity. We found that in Aβ+ individuals pTau‐217 and NTA‐tau showed the highest association with tau PET positivity. Combining pTau‐217 and NTA‐tau resulted in the strongest agreement with the tau PET‐based classification.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".