Exercise interventions in multiple sclerosis rehabilitation need better reporting on comorbidities: a systematic scoping review
Bibliographic record
Abstract
OBJECTIVE: To identify the extent to which exercise intervention studies in multiple sclerosis rehabilitation are addressing comorbidities and if researchers consider comorbidities as possible moderators or mediators of exercise outcomes. METHODS: Five databases were searched from inception to January 8, 2016, for exercise-related terms in combination with multiple sclerosis. Studies were screened and limited to randomized control trials, full text, and English language. We assessed whether comorbidities were excluded or included, how they were reported and described, and if they were examined as possible moderators or mediators of exercise outcomes. RESULTS: We reviewed 99 articles that included various exercise interventions, where the most common were general multi-faceted exercise training ( n=34), cardiovascular training ( n=18), progressive-resistance training ( n=12), and balance and gait training ( n=12). In total, 77 of 99 studies reported one or more comorbidities as an exclusion criterion. The most commonly excluded comorbidities were cardiovascular diseases, cognitive impairments or psychiatric disorders, and unspecified conditions or contraindications. Only nine studies reported details on excluded participants with comorbidities. Across studies that reported comorbidities of included participants ( n=8), none examined comorbidities as possible moderators or mediators of exercise outcomes. CONCLUSION: Although a variety of exercise interventions have positive outcomes, there is limited evidence that these interventions are generalizable to people with multiple sclerosis who have comorbid conditions.
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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 | 0.036 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".