Cardiorespiratory fitness but not cognition predicts covert worsening of gait variability over two years in people with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Gait is typically symmetrical and consistent and subtle increases in gait variability can suggest loss of neural control. In multiple sclerosis (MS), covert walking changes precede clinical signs, often not detectable on observation, and measurement of gait variability could be a potential biomarker of covert neurodegeneration. Both cognition and fitness could influence changes in gait variability. This study aimed to examine gait variability over two years in clinically stable people with MS and determine whether fitness or cognition could predict change in gait variability. RESEARCH QUESTION: Does gait variability serve as a longitudinal biomarker in people with MS, and is fitness or cognition protective against changes in gait variability? METHODS: ) during a graded exercise test using a whole-body recumbent stepper. They performed self-selected walking on an instrumented walkway at initial assessment (T1) and after two years (T2), and stride time variability (STV) was measured as the coefficient of variation of stride time. RESULTS: at T1 was a significant predictor of STV at T2 (β = -0.395, p = .014), accounting for 11.4 % of the variance. Cognition at T1 did not predict changes in STV. SIGNIFICANCE: Lower cardiorespiratory fitness, but not cognition, predicted worsening gait variability over two years. Gait variability may be a sensitive biomarker of covert gait changes not apparent to an observer.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".