Safety of exercise training in multiple sclerosis: An updated systematic review and meta-analysis
Bibliographic record
Abstract
Background: A review of the safety profile of exercise training in multiple sclerosis (MS) has not been conducted since 2013. Objective: We undertook a systematic review and meta-analysis of randomised controlled trials (RCTs) of exercise training published since 2013 and quantified estimated population risks of clinical relapse, adverse events (AE) and serious adverse event (SAE). Methods: Articles reporting safety outcomes from comparisons of exercise training with non-exercise among persons with MS were identified. The risk of bias was established from study’s internal validity assessed using Physiotherapy Evidence Database (PEDro). Rates and estimated mean population relative risks (RRs; 95% confidence interval (CI)) of safety outcomes were calculated, and random-effects meta-analysis estimated the mean RR. Results: Forty-six interventions from 40 RCTs ( N = 1780) yielded 46, 40 and 39 effects for relapse, AE, adverse effects and SAE, respectively. The mean population RRs ((95% CI), p-value) for relapse, AE and SAE were 0.95 ((0.61, 1.48), p = 0.82), 1.40 ((0.90, 2.19), p = 0.14) and 1.05 ((0.62, 1.80), p = 0.85), respectively. No significant heterogeneity is observed for any outcome. Conclusion: In studies that reported safety outcomes, there was no higher risk of relapse, AE, adverse effects or SAE for exercise training than the comparator. Exercise training may be promoted as safe and beneficial to persons with MS.
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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".