Reference charts of plasma p‐tau217 as a pre‐screening tool in AD clinical trials
Bibliographic record
Abstract
BACKGROUND: Plasma phosphorylated tau (p-tau) has been used as a pre-screening tool in clinical trials. Using plasma p-tau for pre-screening may help identify individuals more likely to be Aβ-PET positive, reducing the number of PET scans needed at enrollment and decreasing recruitment costs. This study aims to evaluate the cost-effectiveness of plasma p-tau using liberal, intermediate, and conservative thresholds as pre-screening tools in clinical trials across the AD spectrum. METHOD: We studied 1,673 individuals across the AD spectrum from five cohorts: 707 cognitively unimpaired(CU) and 966 cognitively impaired(CI) with plasma p-tau217 measures and Aβ-PET. We tested three threshold methods:liberal(95% sensitivity), intermediate (Youden index), and conservative(95% specificity). The recruitment sample size was calculated based on p-tau217 assay sensitivity, specificity, and Aβ positivity prevalence for a final sample size of 1,000 based on recent phase 3 trials. RESULT: The intermediate threshold was the most cost-effective, followed by the conservative. Surprisingly, the liberal threshold did not outperform the strategy of screening only with Aβ-PET (Figure 1). SAMPLE SIZE: In CU, pre-screening with the intermediate threshold increased the initial sample size by 30% compared to the non-pre-screened group (n=5,000), while in CI, it increased by 28%(n=2,128). The number of Aβ-PET scans was reduced by 64% in CU and 46% in CI. Trial Cost: In CU, the intermediate threshold reduced costs by 59% compared to no pre-screening ($24M). In CI, costs were reduced by 39% compared to no pre-screening ($9.4M). We tested the pre-screening method using other p-tau217 assays and epitopes and observed reductions in Aβ-PET scans and recruitment costs for all p-tau assays (Figure 2). Using our models and data, we developed a free online tool that allows users to calculate the sample size needed when using plasma p-tau as a pre-screening method for recruiting individuals for clinical trials. This tool will be released with our publication at AAIC (Figure 3). CONCLUSION: Our study demonstrates that plasma p-tau217 is an effective pre-screening tool for clinical trials, with the intermediate threshold being the most cost-effective for both CU and CI. This approach reduces the need for Aβ-PET scans and optimizes recruitment time and costs, potentially enhancing the efficiency of clinical trial designs within the AD spectrum.
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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 | 0.089 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".