Predictive role of gait parameters and MRI markers in assessing cognitive decline in CSVD patients
Bibliographic record
Abstract
BACKGROUND: Cerebral small vessel disease (CSVD) is a major contributor to cognitive decline and gait abnormalities in the elderly population. Understanding the relationship between these impairments can aid in early detection and intervention. This study investigated the association between gait performance and cognitive decline in patients with CSVD, focusing on specific gait parameters as potential clinical markers of cognitive impairment. METHODS: This cross-sectional study included 95 elderly patients with confirmed atherosclerotic CSVD at Guangzhou Geriatric Hospital. Participants underwent comprehensive gait assessments using the Timed Up and Go (TUG) test, Berg Balance Scale (BBS), and Short Physical Performance Battery (SPPB). Cognitive function was evaluated using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Imaging markers, including total magnetic resonance imaging (MRI) CSVD burden and Fazekas scale score, were analyzed. RESULTS: Participants with cognitive decline performed significantly worse on the TUG test, BBS, and SPPB, particularly on the SPPB sit-to-stand component. The total MRI CSVD burden and Fazekas scale scores were significantly higher in the cognitive decline group. Deep cerebral microbleeds and enlarged perivascular spaces in the basal ganglia were more prevalent in individuals with cognitive decline. The association between the SPPB sit-to-stand score and cognitive decline remained significant after adjusting for confounders (OR 0.44; 95% confidence interval (CI) 0.21 to 0.94; p = 0.034). CONCLUSION: This study highlighted the significant relationship between gait performance and cognitive decline in elderly patients with CSVD. In particular, the sit-to-stand component emerged as a robust predictor of cognitive impairment, suggesting its potential as a valuable clinical marker for early detection. The incorporation of gait assessments into routine clinical evaluations can enhance early intervention efforts, improve patient outcomes, and inform healthcare policies. Further longitudinal and multicenter studies are warranted to validate these findings and to explore the underlying mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".