Education for fatigue management in people with multiple sclerosis: Systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Fatigue is a common and disabling symptom in multiple sclerosis (MS). Educational interventions have shown potential to reduce fatigue. The aim was to systematically review the current best evidence on patient education programmes for MS-related fatigue. METHODS: This was a systematic review and meta-analysis following Cochrane methodology. A systematic search was conducted in eight databases (September 2023). Moreover, reference lists and trial registers were searched and experts in the field were contacted. Randomized controlled trials were included evaluating patient education programmes for people with MS with the primary aim of reducing fatigue. RESULTS: In total, 1176 studies were identified and assessed by two independent reviewers; 15 studies (1473 participants) were included. All interventions provided information and education about different aspects of MS-related fatigue with different forms of application, some with components of psychological interventions. Amongst those, the most frequently applied were cognitive behavioural therapy (n = 5) and energy-conservation-based approaches (n = 4). Studies differed considerably concerning mode of intervention delivery, number of participants and length of follow-up. Interventions reduced fatigue severity (eight studies, n = 878, standardized mean difference -0.28; 95% confidence interval -0.53 to -0.03; low certainty) and fatigue impact (nine studies, n = 824, standardized mean difference -0.21; 95% confidence interval -0.42 to 0.00; moderate certainty) directly after the intervention. Mixed results were found for long-term effects on fatigue, for secondary endpoints (depressive symptoms, quality of life, coping) and for subgroup analyses. CONCLUSION: Educational interventions for people with MS-related fatigue may be effective in reducing fatigue in the short term. More research is needed on long-term effects and the importance of specific intervention components, delivery and context.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| 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".