Plasma p‐tau217 identifies cognitively normal older adults who will develop cognitive impairment in a 10‐year window
Bibliographic record
Abstract
Abstract INTRODUCTION We assessed the prognostic accuracy of plasma p‐tau217 in predicting the progression to mild cognitive impairment (MCI) in cognitively unimpaired (CU) individuals over a mean follow‐up of 5.65 years after plasma collection (range 1.01–10.47). METHODS We included 215 participants from the PREVENT−AD cohort with plasma Aβ 42/40 and p‐tau217, 159 with cerebrospinal fluid (CSF) Aβ 42/40 and p‐tau217, and 155 with 18 F‐NAV4694 and 18 F‐flortaucipir PET scans. MCI progression was determined by multidisciplinary consensus among memory experts blind to biomarker and genetic information. RESULTS Cox proportional hazard models indicated a greater progression rate in A+T+ plasma and A−T+ plasma compared to A−T− plasma individuals (HR = 7.81 [95% CI = 3.92 to 15.59] and HR = 4.25 [1.60–11.31] respectively). Similar results were found with CSF (HR = 3.63 [1.72–7.70]) and PET (HR = 9.30 [3.67–23.55]). DISCUSSION Plasma p‐tau217 is a prognostic marker for identifying individuals who will develop cognitive impairment within ten years. Highlights Elevated plasma p‐tau217 levels in CU individuals indicate future clinical progression. Adding plasma Aβ 42/40 status to p‐tau markers did not improve the prediction to MCI. All individuals with abnormal tau PET measured in a temporal meta‐ROI progressed to MCI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".