Combined Efficacy of Foot Reflexology and Back Massage on Pain and Fatigue in Patients Undergoing Hemodialysis
Bibliographic record
Abstract
Background: Multiple treatment modalities have been used to treat complications such as pain and fatigue in patients undergoing hemodialysis. However, the combined effectiveness of reflexology and back massage (BM) in relieving pain and fatigue in patients undergoing hemodialysis is limited and this study aims to fill this research gap. Materials and methods: A pre- and post-test experimental study design was adopted in which 60 patients (n = 60) undergoing dialysis were randomized into two groups of 30 each using a simple randomization technique. The subjects of experimental group I received foot reflexology (FR) and BM, while experimental group II received only BM. The intervention lasted 2 days per week for 4 weeks. To examine the effectiveness of the treatment both before the intervention and at the end of the fourth week, two variables were evaluated: (i) pain intensity, which was measured using the visual analogue scale (VAS) and (ii) fatigue experienced by patients, which was measured using the Fatigue Severity Scale (FSS). Results: Subjects treated with FR and BM showed better reduction in VAS (mean difference: 1.06, 95% confidence interval (CI): 0.299-1.834, p < 0.05) and FSS (mean difference: 6.61, 95% CI: 0.230-11.90, p < 0.05) than the subjects exposed to BM only, with a significance level of 0.05. Conclusion: The combination of FR and BM has been found to be significantly more effective than BM alone in managing the health risks of pain and fatigue in hemodialysis patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".