Gait and balance impairments in patients with subcortical vascular cognitive impairment
Bibliographic record
Abstract
Abstract Background Subcortical vascular cognitive impairment (SVCI) is a subtype of vascular cognitive impairment associated with extensive cerebral small vessel diseases (CSVDs) imaging biomarkers. The objectives of this work were to study the existence and patterns of gait and balance impairments in patients with SVCI due to CSVDs. Methods The study was conducted on 28 newly diagnosed SVCI patients and 22 healthy control subjects (HCS) submitted to the advanced activity of daily living scale (AADLs), Berg balance test (BBT), Montreal Cognitive Assessment Scale (MoCA), computerized dynamic posturography (CDP), vision-based 3-D skeletal data gait analysis, and brain MRI volumetric assessment. Results SVCI patients showed a significant decrease in AADLs as well as total cerebral white matter volume, total cerebral cortical volume, and mean cortical thickness which were proportional to the degree of cognitive impairment as measured by the MoCA score. Regarding CDP analysis, patients with SVCI revealed prolongation of cancelation time and spectral power for mid- and high frequencies in dynamic positions. In respect to gait analysis, there were significant decreases in mean stride length and mean cadence as well as increases in mean step width and left to right step length difference in the SVCI group compared to HCS while doing a single task. These variables get highly significant during the dual-task performance with a p value < 0.001 for each one. Conclusion Patients with SVCI suffer from gait and balance impairments that are proportional to the severity of their cognitive decline and greatly impair their ADLs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".