Gait Disorders and Magnetic Resonance Imaging Characteristics in Older Adults with Cerebral Small Vessel Disease
Bibliographic record
Abstract
Background: This study aims to explore the features of gait disorders with cerebral small vessel disease (CSVD), and results from magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI) were analyzed. Methods: The 139 patients with CSVD were divided into two groups by the Tinetti scale scores: the gait disorder (GD) group with a score <24 (63 patients) and the normal gait (GN) group with a score ≥24 (76 patients). A series of scales and 3.0T MRI with DTI were used to analyze the correlation between the abnormal gaits and imaging findings. Results: The differences in the Barthel Index, Unified Parkinson’s Disease Rating Scale part III, and Montreal Cognitive Assessment (MoCA) scores between the two groups were significant (p < 0.05), and there were significant correlations between MoCA and total gait scores (r = 0.201, p = 0.002). The GD group had a more degraded gait score, widened gait base, and degraded gait length than the GN group (p < 0.05). There were significant differences between the two groups (p < 0.05) in white matter (WM) hyperintensities (WMH) of the Fazekas scale grade 2–3 and lacunes. The GD group had a greater total MRI burden than the GN group (p < 0.05). In DTI parameters, the GD group had lower fractional anisotropy (FA) and higher mean diffusion (MD) values in WM tracts in many areas around the ventricles (family-wise error corrected, p < 0.05). Significant correlations were observed between FA and the total gait score (r = 0.467, p < 0.01), and also between MD and total gait score (r = –0.422, p < 0.01). Conclusions: Patients with CSVD with gait disorders had more WMH of Fazekas scale grade 2–3, lacunes, and total MRI burden than the GN patients, and those with gait disorders may suffer from demyelination of nerve fibers and damage to the fibers’ microstructures.
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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 | 0.000 | 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) | 0.001 | 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".