Gait instability is a more specific predictor of corticospinal tract function than gait speed in clinically stable multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) research requires new, more sensitive, behavioral biomarkers that map to subtle central nervous system injury. Although gait speed, as measured using the Timed 25 Foot Walk Test, is used clinically to track MS progression, it is less useful in people with MS who do not have overt gait impairment. This study aimed to identify specific spatiotemporal gait parameters that predict corticospinal tract (CST) function in individuals with MS. We recruited consecutive patients attending a neurology clinic and evaluated CST excitatory and inhibitory function using single pulse transcranial magnetic stimulation of the primary motor cortex representation of the first dorsal interosseous muscle. We generated excitatory and inhibitory recruitment curves by calculating the area under the curve for motor-evoked potential amplitudes and cortical silent period durations, respectively, across stimulation intensities from 105 to 155% of active motor threshold in 10% increments. Spatiotemporal gait parameters were assessed using an electronic walkway. We built predictive models with gait parameters as the predictors and CST function as the outcome. We evaluated 78 individuals with MS (58 females). Longer distance of the center of pressure movement during single support was the strongest predictor of higher excitability (lower active motor threshold; accounting for 25.8% of variance, R² = 0.258), while less time in double support accounted for a smaller portion of variability in excitatory recruitment curve (13.3% variance explained, R² = 0.133). For inhibitory CST function, slower stride time (30.5% variance explained, R² = 0.305) and wider stride (6.3% variance explained, R² = 0.063) predicted greater inhibition. Notably, in all models, measures of gait stability, not gait speed, predicted CST function. Our results suggest that even among people with MS who have normal gait speed and can easily cross an urban intersection, subtle postural control impairments exist which may not be apparent to them or to their clinician.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".