Mediating Effects of Fatigue and Sleep Quality on Uremic Pruritus and Quality of Life Among Hemodialysis Patients: A Cross‐Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Uremic pruritus is a common and distressing symptom among patients undergoing hemodialysis, frequently accompanied by fatigue and poor sleep quality. These symptoms collectively impair quality of life (QoL), yet their interrelationships remain unclear. To examine whether fatigue and sleep quality mediate the relationship between uremic pruritus and QoL in hemodialysis patients. METHODS: A cross-sectional study using quantitative mediation analysis. A total of 175 hemodialysis patients from three hospital-affiliated dialysis centers in South Korea completed validated self-report measures assessing uremic pruritus, fatigue, sleep quality, and QoL. Mediation analysis was conducted using Baron and Kenny's framework, Sobel test, and bootstrapping. FINDINGS: Uremic pruritus was significantly correlated with fatigue (r = 0.30, p < 0.001) and sleep quality (r = 0.53, p < 0.001), and negatively correlated with QoL (r = -0.29, p < 0.001). Fatigue (B = -0.3, 95% CI: -0.5 to -0.1) and sleep quality (B = -0.2, 95% CI: -0.4 to -0.1) were significantly associated with both uremic pruritus and QoL. The final model accounted for 40% of the variance in QoL. CONCLUSIONS: Uremic pruritus indirectly affects QoL through its impact on fatigue and sleep quality. This suggests that its influence operates via interconnected symptoms rather than directly. The findings support the need for integrated symptom management approaches in dialysis care. Interventions targeting fatigue and sleep quality may be effective in reducing the burden of pruritus and improving daily functioning and well-being in patients undergoing hemodialysis. PREPRINT STATEMENT: This manuscript has not been previously published and is not under consideration elsewhere. If the manuscript is posted on a preprint server, the authors will update it with a link to the final published version. STATISTICAL COMPLIANCE STATEMENT: The statistics were checked prior to submission by an expert statistician, Ilhyun Lee, Email: tarra@statedu.com.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".