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Record W7117325960 · doi:10.1002/alz70859_105750

Reference charts of plasma p‐tau217 as a pre‐screening tool in AD clinical trials

2025· article· en· W7117325960 on OpenAlexaff
Pamela C.L. Ferreira, Guilherme Povala, Bruna Bellaver, Guilherme Bauer‐Negrini, Cristiano Schaffer Aguzzoli, Firoza Z Lussier, João Pedro Ferrari‐Souza, Douglas Teixeira Leffa, Marina Scop Madeiros, Carolina Soares, Helmet T. Karim, Eduardo R. Zimmer, Chang Hyung Hong, Hyun Woong Roh, Ann D Cohen, Pedro Rosa‐Neto, Dana Tudorascu, Beth E. Snitz, Thomas K Karikari, Sang Joon Son, Tharick A. Pascoal

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsClinical trialChartCalibrationMEDLINE

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.089
metaresearch head score (Gemma)0.277
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.089
Threshold uncertainty score0.469

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0890.277
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.005

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.

Opus teacher head0.172
GPT teacher head0.464
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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