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‐tau 181 , p‐tau 217 , and p‐tau 231 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‐tau 181 (AUC = 76%) and p‐tau 231 (AUC = 82%) assessments performed inferior to CSF p‐tau 181 (AUC = 87%) and p‐tau 231 (AUC = 95%) for amyloid‐PET positivity. However, plasma p‐tau 217 (AUC = 91%) had diagnostic performance indistinguishable from CSF (AUC = 94%) for amyloid‐PET positivity. DISCUSSION Plasma and CSF p‐tau 217 had equivalent diagnostic performance for biomarker‐defined AD. Our results suggest that plasma p‐tau 217 may help reduce the need for invasive lumbar punctures without compromising accuracy in the identification of AD. Highlights p‐tau 217 in plasma performed equivalent to p‐tau 217 in CSF for the diagnosis of AD, suggesting the increased accessibility of plasma p‐tau 217 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‐tau 181 and plasma p‐tau 231 performed worse than p‐tau 181 and p‐tau 231 in CSF for AD diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".