Effect of Muscle Relaxation on Hemodialysis Patients’ Pain
Bibliographic record
Abstract
Aims: Dialysis patients have experienced some degree of pain, especially foot pain.Some complementary interventions such as muscle relaxation are effective in relieving pain.This study was performed with the aim of assessing the effect of muscle relaxation on hemodialysis patients' pain.Materials & Methods: This randomized controlled clinical trial was conducted on 90 hemodialysis patients of Khatamolanbia and Imam Ali hemodialysis centers of Zahedan during 2013 and 2014.The patients were chosen by purposive sampling based on inclusion criteria and randomly divided into control and experimental groups.Pain intensity was measured by McGill questionnaire before intervention.Then, Benson muscle relaxation was taught to patients' of case group and was performed by them for 15-20 minutes twice a day for a month.The control group received no training.The pain intensity of two groups was compared after one month.The data were analyzed using Chisquare, independent T and Mann-Whitney tests by SPSS 21 software.Findings: Most of the patients were men, married, housekeeper with under diploma education and the mean age of them was 43.0±15.0years.There was a significant decrease in pain intensity in the intervention group compared to the control (p=0.03). Conclusion: The muscle relaxation technique can be employed to reduce pain 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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".