White matter atrophy in cerebral amyloid angiopathy
Bibliographic record
Abstract
Objective We postulated that cerebral amyloid angiopathy (CAA) is associated with white matter atrophy (WMA) and that WMA can be related to cognitive changes in CAA. Methods White matter volume expressed as percent of intracranial volume (pWMV) of prospectively enrolled patients without dementia diagnosed with probable CAA was compared to age-matched healthy controls (HC) and patients with Alzheimer disease (AD). Cognitive scores were also sought to understand the potential effects of WMA on cognitive function. Results Patients with CAA (n = 72) had significantly lower pWMV (27.97% ± 2.63) when compared to age-matched HC (n = 72; mean difference [MD], 2.38%; p < 0.0001) and patients with AD (n = 72; MD, 1.57%; p < 0.0001). Differences were most pronounced in the posterior occipital regions in both comparisons. When comparisons were restricted to groups of patients with CAA but no intracerebral hemorrhage (n = 32) or hypertension (n = 32), and age-matched HC and AD, the significant differences were unaltered. Within the CAA cohort, higher age, lobar microbleed counts, and presence of hypertension were associated with lower pWMV (p = 0.0007, p = 0.031, and p = 0.003, respectively). All associations remained independent in multivariable analyses. Within the CAA cohort, higher pWMV independently correlated with better scores of executive function. Conclusions Patients with CAA show WMA when compared to age-matched HC and patients with AD. WMA independently correlates with the number of lobar microbleeds, a marker of CAA severity. Consistent spatial patterns of WMA especially in posterior regions might be related to CAA. The association between WMA and measures of executive function suggests that WMA might represent an important mediator of CAA-related neurologic dysfunction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".