Investigation of the Effect of Reiki on Pain, Fatigue, and Itching in Hemodialysis Patients: Randomized Controlled Trial
Bibliographic record
Abstract
INTRODUCTION: The management of symptoms associated with hemodialysis, which adversely affect patients, increases their quality of life. Complementary integrative therapies such as Reiki are used in symptom management. This study aimed to investigate the effects of Reiki on pain, fatigue, and itching in hemodialysis patients. METHODS: The study had a randomized controlled design and was conducted in three dialysis centers with a total of 74 hemodialysis patients, 37 in the intervention group, and 37 in the control group. A total of 10 sessions of Reiki were administered to the patients in the intervention group twice a week for 5 weeks, while the patients in the control group received routine hemodialysis treatment. The data of the study were collected using the Patient Identification Form, the Patient Clinical Parameters Form, the Visual Analog Scale, the McGill Pain Questionnaire, the Piper Fatigue Scale, and the 5-D Itch Scale. FINDINGS: It was determined that there was a statistically significant decrease in the pain, fatigue, and itching levels of the patients in favor of the intervention group in the second and third measurements (p < 0.05). Although there was no change in the pain and fatigue levels of the patients in the control group, the levels of itching increased statistically and significantly (p < 0.05). DISCUSSION: The findings suggest that Reiki has an effect on pain, fatigue, and itching in hemodialysis patients. TRIAL REGISTRATION: ClinicalTrials.gov: NCT05531175.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".