Rehabilitation interventions for improving gait for people with multiple sclerosis: A scoping review of reviews
Bibliographic record
Abstract
BACKGROUND: Evidence for rehabilitation interventions to improve gait outcomes for people with multiple sclerosis (PwMS) has not been previously mapped, despite the prevalence and impact of gait impairment in MS. This review of reviews aimed to map the scope of physical based rehabilitation interventions intended to improve objective gait outcomes in PwMS. METHODS: We conducted a scoping review of reviews searching five databases, from inception to January 2024, focusing on "Multiple Sclerosis", "Rehabilitation," "gait," and "systematic review". A narrative synthesis of evidence was used due to the significant data heterogeneity. Evidence Mapping, using the corrected covered area (CCA), was applied to assess the degree of overlap among included reviews. Quality appraisal was conducted using the Joanna Briggs Institute (JBI) Checklist for Systematic Reviews and Research Syntheses. RESULTS: Out of 409 identified reviews, 67 met inclusion criteria and 56 were classified as high-quality reviews. Exercise training was the most commonly investigated intervention, followed by whole-body vibration and robot-assisted gait training. The most frequently used gait assessment tools were 10-Meter Walk Test and Timed 25-Foot Walk Test (for gait speed), 6-Minute Walk Test (for gait endurance), and Timed Up and Go Test (for functional mobility). CONCLUSION: This review highlights a substantial body of evidence, yet also reveals considerable overlap reported in CCA scores across the included reviews for most of interventions. To maximize clinical impact, future research must move beyond broad generalizations and focus on developing targeted evidence for specific groups of PwMS based on age groups, disability level, and functional goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".