CSF biomarkers and plasma p‐tau181 as predictors of longitudinal tau accumulation: Implications for clinical trial design
Bibliographic record
Abstract
INTRODUCTION: Clinical trials targeting tau in Alzheimer's disease (AD) need to recruit individuals at risk of tau accumulation. Here, we studied cerebrospinal fluid (CSF) biomarkers and plasma phosphorylated tau (p-tau)181 as predictors of tau accumulation on positron emission tomography (PET) to evaluate implications for trial designs. METHODS: We included older individuals who had serial tau-PET scans, baseline amyloid beta (Aβ)-PET, and baseline CSF biomarkers (n = 163) or plasma p-tau181 (n = 74). We studied fluid biomarker associations with tau accumulation and estimated trial sample sizes and screening failure reductions by implementing these markers into participant selection for trials. RESULTS: P-tau181 in CSF and plasma predicted tau accumulation (r > 0.36, P < .001), even in AD-continuum individuals with normal baseline tau-PET (A+T-; r > 0.37, P < .05). Recruitment based on CSF biomarkers yielded comparable sample sizes to Aβ-PET. Prescreening with plasma p-tau181 reduced up to ≈50% of screening failures. DISCUSSION: Clinical trials testing tau-targeting therapies may benefit from using fluid biomarkers to recruit individuals at risk of tau aggregation.
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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 | 0.167 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".