White Matter Hyperintensity as a Vascular Contribution to the AT(N) Framework
Bibliographic record
Abstract
The AT(N) framework enables the classification of an individual within the biological Alzheimer's disease (AD) continuum by pairing the cognitive stage with the biomarker status of amyloid-beta (Aβ, A), tau (T) and neurodegeneration (N). AD is a multifactorial disease that may involve different pathogenic mechanisms such as cerebrovascular disease (CVD). Therefore, biomarkers of these mechanisms can be added to the AT(N) framework to enhance the biomarker characterization of individuals within the AD continuum. In AD, white matter hyperintensities (WMH) which are postulated to develop as a result of chronic ischemia from small vessel CVD are shown to play a role in the aetiology. However, the interplay of WMH with Aβ and tau pathophysiology in AD remains unclear. In this review, we summarized the studies that evaluated the associations between WMH and AD pathophysiology (Aβ and tau). We found that the evidence supporting the association of WMH with Aβ was mixed, and this may be explained by the relative contributions of WMH due to its differential load and anatomical distribution. More studies are also needed to determine the association of WMH with tau pathology. Future longitudinal studies with harmonized methodologies to quantify WMH and account for the anatomical differences of WMH are required to validate the relationship between WMH and AT(N) biomarkers. This will allow a clearer understanding of the utility of WMH as a vascular biomarker in the AT(N) framework. Novel CVD biomarkers will also have the potential to further elucidate the contributions of CVD to the AD pathophysiology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".