Meta-Analysis of Three Different Types of Fatigue Management Interventions for People with Multiple Sclerosis: Exercise, Education, and Medication
Bibliographic record
Abstract
Fatigue is a common symptom of multiple sclerosis (MS) with negative impacts extending from general functioning to quality of life. Both the cause and consequences of MS fatigue are considered multidimensional and necessitate multidisciplinary treatment for successful symptom management. Clinical practice guidelines suggest medication and rehabilitation for managing fatigue. This review summarized available research literature about three types of fatigue management interventions (exercise, education, and medication) to provide comprehensive perspective on treatment options and facilitate a comparison of their effectiveness. We researched PubMed, Embase, and CINAHL (August 2013). Search terms included multiple sclerosis, fatigue, energy conservation, Amantadine, Modafinil, and randomized controlled trial. The search identified 230 citations. After the full-text review, 18 rehabilitation and 7 pharmacological trials targeting fatigue were selected. Rehabilitation interventions appeared to have stronger and more significant effects on reducing the impact or severity of patient-reported fatigue compared to medication. Pharmacological agents, including fatigue medication, are important but often do not enable people with MS to cope with their existing disabilities. MS fatigue affects various components of one's health and wellbeing. People with MS experiencing fatigue and their healthcare providers should consider a full spectrum of effective fatigue management interventions, from exercise to educational strategies in conjunction with medication.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 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 | 0.001 |
| Research integrity | 0.002 | 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".