A New Approach to Haemodialysis: Nurse-Led Dual Intervention Eases AV-Fistula Puncture Pain and Discomfort
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) affects approximately 10% of the global population and often progresses to end-stage renal disease, necessitating hemodialysis. Arteriovenous (AV) fistula puncture is a routine yet painful procedure, highlighting the need for effective pain management strategies. Objective: This study aimed to evaluate the effectiveness of a Nurse-Led Dual Intervention, combining cryotherapy and virtual reality, in reducing pain and discomfort during AV fistula puncture among hemodialysis patients. Methods: A randomized controlled trial was conducted with 60 hemodialysis patients assigned to either an experimental or control group. Pain and discomfort were assessed using McGill's Pain Rating Scale and Borg's Perceived Discomfort Rating Scale. The intervention involved the simultaneous application of cryotherapy and virtual reality distraction during AV fistula puncture, while the control group received standard care. Results: Post-intervention analysis demonstrated a 43.62% reduction in symptom scores in the experimental group compared to a 2.80% reduction in the control group. Significant decreases in pain intensity and discomfort scores were observed in the experimental group. Additionally, statistical analysis revealed meaningful associations between socio-demographic variables and pain management outcomes. Conclusion: The findings suggest that integrating non-pharmacological interventions, such as cryotherapy and virtual reality, can significantly improve patient comfort and quality of life during hemodialysis. Future research should explore the broader clinical applications of this dual intervention for pain management in various healthcare settings.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".