Effects of warm or cold compresses applied to the legs during hemodialysis on cramps, fatigue, and patient comfort: A placebo‐controlled randomized trial
Bibliographic record
Abstract
Abstract Introduction Muscle cramps and fatigue are common complications in hemodialysis patients and have been associated with reduced patient comfort. Among the complementary therapies advocated for the management of these complications have been the application of warm or cold compresses to the extremities during a hemodialysis treatment. In this study, we compared the effects of warm or cold compresses application on cramping, fatigue, and patient comfort. Methods This placebo‐controlled randomized trial was done in 69 patients, who were stratified and randomly allocated to three treatment arms. Two of the three groups included an intervention; application of either warm ( n = 23) or cold ( n = 23) compresses to the extremities during dialysis. The third group served as a placebo control ( n = 23). The study period comprised 12 hemodialysis sessions. One week after the completion of the intervention, a follow‐up dialysis session was also evaluated. Data were collected at baseline ( t 0 ), during each of 12 intervention sessions ( t 1 — t 12 ), and at the follow‐up session t 13 . Cramps, fatigue, and patient comfort were evaluated using the Cramp Episode Follow‐up Chart, Piper's Fatigue Scale, and the Hemodialysis Comfort Scale, respectively. Results In both the intervention and follow‐up sessions, cramping and fatigue were lower, and comfort was higher in each of the intervention groups compared to placebo controls Application of warm compresses was superior to use of cold compresses. Discussion Both warm and cold compress administration reduced muscle cramps, fatigue, and hemodialysis comfort 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".