Cognitive Impact of Lacunar Infarcts and White Matter Hyperintensity Volume
Bibliographic record
Abstract
BACKGROUND: Subcortical lacunar infarcts and white matter hyperintensities (WMH) are common neuroradiological findings, but few studies associate between these insults and cognition in a community-dwelling population. METHODS: The Dallas Heart Study is a population-based initiative whose assessments included demographic and clinical findings including brain MRI and the Montreal Cognitive Assessment (MoCA). The presence and number of lacunes in subjects aged over 55 years were assessed by study physicians. The WMH volume was measured by an automated method. The association between the presence and number of lacunar infarcts and of WMH volume with the total MoCA score and subdomains was assessed using linear regression with adjustment for age, gender and self-reported ethnicity. RESULTS: In 609 subjects with valid data, both the presence and the increasing number of lacunes were associated with lower MoCA scores, even after adjusting for demographic variables. The presence of lacunes was also associated with lower scores in the memory, executive and attention subdomains. The WMH volume was not significantly associated with the MoCA score. CONCLUSION: The presence and increasing number of lacunes in midlife is associated with a lower performance in multiple domains of a cognitive screening measure after adjusting for demographic factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".