Gait and falls in cerebral small vessel disease: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Gait impairment contributes to falls and frailty. Some studies suggest that cerebral small vessel disease (CSVD) is associated with gait impairment in the general population. We systematically reviewed and meta-analysed the literature on associations of CSVD with gait impairment and falls. METHODS: The protocol was published in PROSPERO (CRD42021246009). Searches of Medline, Cochrane and Embase databases were conducted on 30 March 2022. Cross-sectional and longitudinal studies of community-dwelling adults were included, reporting relationships between diagnosis or neuroimaging markers of CSVD and outcomes related to gait or falls. Partial correlation coefficients were calculated and pooled using a random-effects model for meta-analysis. RESULTS: The search retrieved 73 studies (53 cross-sectional; 20 longitudinal). Most studies reported an association between CSVD and gait impairments or falls risk: 7/7 studies on CSVD score or diagnosis, 53/67 studies on white matter hyperintensities (WMHs), 11/21 studies on lacunar infarcts, 6/15 studies on cerebral microbleeds and 1/5 studies on perivascular spaces. Meta-analysis of 13 studies found that higher WMH volume was mildly correlated with lower gait speed, in all studies (r = -0.23, 95% confidence interval: -0.33 to -0.14, P < 0.0001). However, there was significant heterogeneity between studies (I2 = 82.95%; tau2 = 0.02; Q = 79.37, P < 0.0001), which was unexplained by variation in age, sex, study quality or if the study adjusted for age. CONCLUSIONS: Findings suggest that CSVD severity is associated with gait impairment, history of falls and risk of future falls. Prevention of CSVD should be part of a comprehensive public health strategy to improve mobility and reduce risk of falls in later life.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".