Association between small vessel disease and slow gait speed in older adults with cognitive impairment
Bibliographic record
Abstract
Subjective cognitive decline and mild cognitive impairment are often associated with gait disturbances, increasing dementia risk. Cerebral small vessel disease (CSVD) may underlie these associations. Objective: To examine the association between CSVD lesion types and locations with slow gait speed in older adults with subjective cognitive decline and mild cognitive impairment. Methods: In this cross-sectional study, 124 older adults met the inclusion criteria. Participants underwent clinical evaluation, gait speed assessment, and brain magnetic resonance imaging. CSVD burden was assessed using the STRIVE-2 criteria and quantified using Fazekas and modified Scheltens scales. Logistic regression analyses were conducted to calculate odds ratios (OR) with 95% confidence intervals. Results: Individuals with slow gait were older, had lower education levels, and a higher prevalence of hypertension. Neuroimaging analysis revealed a significant association between slow gait and global white matter hyperintensities (WMH) burden (Fazekas score ≥2: 34.5 vs. 12.1%, p<0.003; Scheltens score ≥5: 65.8 vs. 42.4%, p<0.010). Regional WMH analysis showed increased burden in frontal and occipital regions in the slow gait group. Lacunar infarcts were more prevalent in the slow gait group (15.2 vs. 3.4%, p=0.028). Multivariate analysis revealed that lacunar infarcts and WMH in specific brain regions remained significant predictors of slow gait, even after adjusting for confounders. Conclusion: CSVD, particularly lacunar infarcts and specific WMH patterns, is associated with slow gait in this population. Early identification and management of CSVD may help mitigate its impact on gait and functional status.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".