What does a structured review of the effectiveness of exercise interventions for persons with multiple sclerosis tell us about the challenges of designing trials?
Bibliographic record
Abstract
OBJECTIVE: The goal of this review is to ascertain the extent to which the current body of research on the role of exercise in multiple sclerosis (MS) provides sufficiently strong evidence to guide regular exercise prescription. METHODS: We searched CINAHL, COCHRANE, EMBASE, and MEDLINE between 1950 and December 2007 with combinations of MeSH terms and keywords. We assessed the methodological quality of selected randomized controlled trials (RCTs) of exercise interventions using the Physiotherapy Evidence Database scale and evaluated the effects of the exercise interventions by calculating effect sizes (ES) for the target outcomes. RESULTS: Eleven RCTs met the criteria, all with acceptable methodological quality. The ES ranged from -0.36 to 3.50 on the target outcomes. Only one study had 95% confidence intervals clearly excluding a value of 0. Measures of body functions and structures and activities were the most common target outcomes of interventions. CONCLUSION: Although there was some evidence to support positive effects of exercise on physical and psychosocial functioning and on quality of life, our review revealed insufficient research in this area, making it difficult to guide regular exercise prescription. Furthermore, it also emphasizes the methodological challenges in these RCTs leading us to believe that there is a great need for high quality RCTs in this area, contributing evidence for regular exercise and physical activity prescription for persons with MS.
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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 | 0.257 | 0.629 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.012 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".