Plasma biomarkers as stand‐alone tests in the diagnosis of Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Several recent studies have provided evidence for high diagnostic performance of plasma biomarkers for AD, with Area Under the Curve values sometimes exceeding 90%. However, positive and negative predictive values are rarely reported yet are critical for interpreting individual‐level screening test results. Method We determined positive and negative predictive values of plasma biomarkers (p‐tau181, p‐tau217, p‐tau231, GFAP and NfL) for biological AD (A+T+, determined either with PET or CSF) in the TRIAD, WRAP, ALFA+, BIODEGMAR, ADNI and McGill memory clinic cohorts (total n = 2219). We also determined positive and negative predictive values for specific clinical scenarios: MCI and mild AD – two populations that may benefit from disease‐modifying therapies in AD. Prevalence estimates of biological AD in these groups were taken from the Mayo Clinic Study of Aging. Result P‐tau217 had the highest positive and negative predictive values of all biomarkers in all cohorts, with above 80% positive and negative predictive values. In individuals with MCI, an etiologically heterogeneous syndrome, plasma p‐tau217 had sufficiently high performance to rule out biological AD in differential diagnosis. In mild AD dementia, plasma p‐tau217 could rule in biological AD, but follow‐up with PET/CSF will likely be needed to confirm the absence of AD. Conclusion Plasma biomarkers have the potential to be used as stand‐alone diagnostic tools in specific clinical scenarios. In MCI, a negative plasma p‐tau217 could rule out AD as a cause of cognitive impairment, but a positive result should be followed up with PET/CSF. In mild AD dementia, a positive plasma p‐tau217 result can rule in biological AD, but a negative result should be followed up with PET/CSF.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".