Optimizing tau‐PET referrals in memory clinics through a blood biomarker workflow
Bibliographic record
Abstract
Abstract Background Blood‐based biomarkers have demonstrated great promise for identifying biomarker‐confirmed Alzheimer's disease. We aimed to evaluate whether blood‐based biomarkers could optimize the referral of memory clinic patients to a tau‐PET exam, which is crucial for prognostic evaluation. Method The study measured various plasma biomarkers (Aβ42/Aβ40, pTau181, pTau217, pTau231, NfL, and GFAP) and compared them with tau‐PET scan results in patients with subjective cognitive decline, mild cognitive impairment, or dementia. Participants were sourced from the Swedish BioFINDER‐2 study (548 individuals) and the TRIAD study (179 individuals). Cutoffs for each biomarker were established at 90%, 95%, and 97.5% sensitivity for detecting tau‐PET‐positivity. We then calculated the percentage of patients below these cutoffs (to potentially avoid unnecessary tau‐PET scans) and the tau‐PET‐positivity rate among those above the cutoffs. Result Plasma pTau217 showed the most promising results. At a 95% sensitivity cutoff in both cohorts, using pTau217 could avoid nearly half of the tau‐PET scans while maintaining a tau‐PET‐positivity rate of approximately 70% in those referred. Furthermore, tau‐PET was strongly associated with subsequent cognitive decline. In the BioFINDER‐2 cohort, tau‐PET predicted cognitive decline only in individuals above the plasma pTau217 referral cutoff, suggesting a more targeted and informative use of tau‐PET scans. Conclusion Plasma pTau217 demonstrates potential as a guiding biomarker for selecting Alzheimer’s disease patients for tau‐PET scans, particularly when accurate prognostic information is clinically valuable. This approach could lead to more efficient and informative use of tau‐PET scans, avoiding unnecessary procedures in patients unlikely to benefit from them.
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 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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".