Do subcortical gray matter volumes and aerobic capacity account for cognitive-motor coupling in multiple sclerosis?
Bibliographic record
Abstract
BACKGROUND: There is evidence of cognitive-motor coupling in multiple sclerosis (MS) such that the slowing of cognitive processing speed correlates with the worsening of walking speed and endurance. OBJECTIVE: The current study first established the presence of cognitive-motor coupling and second examined the possibility that volumes of subcortical gray matter (SGM) structures and aerobic capacity might explain the coupling of cognitive and motor functions in persons with MS. METHODS: We included data from 62 persons with clinically definite MS who underwent assessments of cognitive processing speed, walking performance, and aerobic capacity, and completed magnetic resonance imaging (MRI) within 7 days of the aforementioned assessments. RESULTS: The strong correlations between cognitive processing speed and walking performance were attenuated in magnitude and not statistically significant when controlling for aerobic capacity alone and aerobic capacity and SGM volumes together. The associations between cognitive processing speed and walking performance remained statistically significant when controlling for SGM volumes alone. CONCLUSION: Aerobic capacity may be an important target for neurorehabilitation-based approaches for managing co-occurring cognitive and motor dysfunction in 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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".