Comparative methods for quantifying plasma biomarkers in Alzheimer's disease: Implications for the next frontier in cerebral amyloid angiopathy diagnostics
Bibliographic record
Abstract
Abstract Plasma amyloid beta (Aβ) and tau are emerging as accessible biomarkers for Alzheimer's disease (AD). However, many assays exist with variable test performances, highlighting the need for a comparative assessment to identify the most valid assays for future use in AD and to apply to other settings in which the same biomarkers may be useful, namely, cerebral amyloid angiopathy (CAA). CAA is a progressive cerebrovascular disease characterized by deposition of Aβ40 and Aβ42 in cortical and leptomeningeal vessels. Novel immunotherapies for AD can induce amyloid‐related imaging abnormalities resembling CAA‐related inflammation. Few studies have evaluated plasma biomarkers in CAA. Identifying a CAA signature could facilitate diagnosis, prognosis, and a safer selection of patients with AD for emerging immunotherapies. This review evaluates studies that compare the diagnostic test performance of plasma biomarker techniques in AD and cerebrovascular and plasma biomarker profiles of CAA; it also discusses novel hypotheses and future avenues for plasma biomarker research in CAA.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".