Postural Motor Learning Deficits in People With MS in Spatial but Not Temporal Control of Center of Mass
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is associated with balance deficits resulting in falls and impaired mobility. Although rehabilitation has been recommended to address these balance deficits, the extent to which people with MS can learn and retain improvements in postural responses is unknown. AIM: To determine the ability of people with MS to improve postural control with surface perturbation training. METHODS: A total of 24 patients with mild MS and 14 age-matched controls underwent postural control training with a set pattern of continuous, forward-backward, sinusoidal, and surface translations provided by a force platform. Postural control was then tested the following day for retention. The primary outcome measures were the relative phase and center-of-mass (CoM) gain between the body CoM and the platform motion. RESULTS: People with MS demonstrated similar improvements in acquiring and retaining changes in the temporal control of the CoM despite significant deficits in postural motor performance at the baseline. Both MS and control groups learned to anticipate the pattern of forward-backward perturbations, so body CoM shifted from a phase-lag (age-matched controls [CS] = -7.1 ± 1.3; MS = -12.9 ± 1.0) toward a phase-lead (CS = -0.7 ± 1.8; MS = -6.1 ± 1.4) relationship with the surface oscillations. However, MS patients were not able to retain the changes in the spatial control of the CoM acquired during training. CONCLUSIONS: People with MS have the capacity to improve use of a feed-forward postural strategy with practice and retain the learned behavior for temporal not spatial control of CoM, despite their significant postural response impairments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".