Equivalence of plasma p‐tau217 with cerebrospinal fluid in the diagnosis of Alzheimer's disease
Bibliographic record
Abstract
Abstract INTRODUCTION Plasma biomarkers are promising tools for Alzheimer's disease (AD) diagnosis, but comparisons with more established biomarkers are needed. METHODS We assessed the diagnostic performance of p‐tau181, p‐tau217, and p‐tau231 in plasma and CSF in 174 individuals evaluated by dementia specialists and assessed with amyloid‐PET and tau‐PET. Receiver operating characteristic (ROC) analyses assessed the performance of plasma and CSF biomarkers to identify amyloid‐PET and tau‐PET positivity. RESULTS Plasma p‐tau biomarkers had lower dynamic ranges and effect sizes compared to CSF p‐tau. Plasma p‐tau181 (AUC = 76%) and p‐tau231 (AUC = 82%) assessments performed inferior to CSF p‐tau181 (AUC = 87%) and p‐tau231 (AUC = 95%) for amyloid‐PET positivity. However, plasma p‐tau217 (AUC = 91%) had diagnostic performance indistinguishable from CSF (AUC = 94%) for amyloid‐PET positivity. DISCUSSION Plasma and CSF p‐tau217 had equivalent diagnostic performance for biomarker‐defined AD. Our results suggest that plasma p‐tau217 may help reduce the need for invasive lumbar punctures without compromising accuracy in the identification of AD. Highlights p‐tau217 in plasma performed equivalent to p‐tau217 in CSF for the diagnosis of AD, suggesting the increased accessibility of plasma p‐tau217 is not offset by lower accuracy. p‐tau biomarkers in plasma had lower mean fold‐changes between amyloid‐PET negative and positive groups than p‐tau biomarkers in CSF. CSF p‐tau biomarkers had greater effect sizes than plasma p‐tau biomarkers when differentiating between amyloid‐PET positive and negative groups. Plasma p‐tau181 and plasma p‐tau231 performed worse than p‐tau181 and p‐tau231 in CSF for AD diagnosis.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".