Racial differences in white matter hyperintensity burden in aging, MCI, and AD
Bibliographic record
Abstract
Abstract White matter hyperintensities may be one of the earliest pathological changes in aging and may potentially accelerate cognitive decline. Whether race influences WMH burden has been conflicting. The goal of this study was to examine if race differences exist in WMH burden and whether these differences are influenced by vascular factors [i.e., diabetes, hypertension, body mass index (BMI)]. Participants from the Alzheimer’s Disease Neuroimaging Initiative were included if they had a baseline MRI, diagnosis, and WMH measurements. Ninety-one Black and 1937 White individuals were included. Using bootstrap re-sampling, 91 Whites were randomly sampled and matched to Black participants based on age, sex, education, and diagnosis 1000 times. Linear regression models examined the influence of race on baseline WMHs with and without vascular factors: WMH ∼ Race + Age + Sex + Education + BMI + Hypertension + Diabetes and WMH ∼ Race + Age + Sex + Education . The 95% confidence limits of the t-statistics distributions for the 1000 samples were examined to determine statistical significance. All vascular risk factors had significantly higher prevalence in Black than White individuals. When not including vascular risk factors, Black individuals had greater WMH volume overall as well as in frontal and parietal regions, compared to White individuals. After controlling for vascular risk factors, no WMH group differences remained significant. These findings suggest that vascular risk factors are a major contributor to racial group differences observed in WMHs.
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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 | 0.003 |
| 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) | 0.002 | 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".