Association between cognitive impairment and motor dysfunction among patients with multiple sclerosis: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown that there is a relationship between cognitive impairment (CI) and motor dysfunction (MD) in neurological diseases, such as Alzheimer's and Parkinson's disease. However, there whether CI and MD are associated in patients with multiple sclerosis (MS) is unknown. Here we studied the association between CI and MD in patients with MS and examined if muscle weakness or incoordination, balance impairment, gait abnormalities, and/or increased fall risk are indicators of CI in patients with MS. METHODS: Seventy patients with MS were included in this cross-sectional study. Cognitive impairment was assessed using the Montreal Cognitive Assessment Scale (MoCA), muscle strength using a hand-held dynamometer, and balance, gait, and fall risk assessment using the Tinetti scale. Motor coordination was assessed using the timed rapid alternating movement test for the upper extremity and the timed alternate heel-to-knee test for the lower extremity. RESULTS: There was a significant association between CI and motor coordination, balance, gait, and risk of fall (p < 0.005) but not muscle strength. Stepwise multiple linear regression showed that 22.7% of the variance in the MoCA was predicted by the fall risk and incoordination of the upper extremities in the MS population. CONCLUSIONS: CI is significantly associated with motor incoordination, balance impairment, gait abnormality, and increased fall risk. Furthermore, the risk of fall and upper extremity incoordination appeared to be best indicators of CI in patients with MS.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".