Effect of Acupressure on Fatigue in Women With Multiple Sclerosis
Bibliographic record
Abstract
INTRODUCTION: Multiple sclerosis (MS) is the most common cause of progressive neurological disability. The prevalence of MS is much more common in women than men. The women are exposed to a variety of symptoms including fatigue. Acupressure is a noninvasive procedure that can be used to control symptoms including fatigue. The aim of the study was to evaluate the effect of acupressure on fatigue in women with multiple sclerosis. METHODS: A randomized clinical trial was conducted on 100 women with MS at Tehran MS Association. The subjects were equally allocated to experimental group and a placebo group (50 women per group) by blocking randomization method. The experimental group were received acupressure, at the true points (ST36, SP6, LI4) and the placebo group, were received touching at the same points. Fatigue was measured by a Fatigue Severity Scale (FSS) in the groups at immediately prior to, two and four weeks after the beginning of the intervention. The data was analyzed using descriptive and inferential statistics by SPSS version 17. RESULTS: The findings indicated no differences in demographic characteristics and the severity of fatigue at the baseline in two groups (p=0.54). But there were significant reductions of the mean score of fatigue in the experimental group compared to the placebo group immediately, two and four weeks after the intervention respectively (p=0.03, p?0/001, p=0.04). CONCLUSION: According to the findings, the study provided an alternative method for health care providers including nurses to train acupressure to the clients with MS to managing their fatigue.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".