Pain-related Factors in Hemodialysis Patients: Pain in Hemodialysis Patients
Bibliographic record
Abstract
Background: Pain is a prevalent issue among patients undergoing hemodialysis (HD). This study aimed to evaluate the prevalence of pain and identify factors associated with pain in HD patients. Methods: Two hundred two HD patients participated in the study. Demographic and clinical data, pain characteristics, and sleep quality were recorded. Symptom burden and pain severity were assessed using the Edmonton Symptom Assessment Scale (ESAS) and the McGill-Melzack Pain (MGP) questionnaire. Results: The majority of participants were male (59.9%), with a mean age of 59.6±12.7 years. Pain was reported by 80.2% of the patients and was significantly more prevalent among females (p=0.001) and individuals with lower educational levels (p=0.005). Median ESAS and MGP scores were 20 (range: 4-84) and 47 (range: 22-84), respectively. Patients reporting pain had significantly higher levels of CRP (p=0.044), parathyroid hormone (p=0.005), and higher ESAS scores (p=0.001). Sleep quality was impaired in 37% of patients. ESAS scores were significantly higher among females (p=0.003), those with impaired sleep quality (p<0.001), and regular analgesic users (p=0.002). MGP scores were significantly elevated in patients with diabetes (p=0.002), lower educational attainment (p=0.022), daily pain occurrence (p<0.001), and poor sleep quality (p<0.001). Additionally, patients with pain in multiple body regions reported higher MGP scores (p<0.001). There was a significant correlation between MGP scores, age (p=0.001), and ESAS scores (p<0.001). Conclusion: Pain is highly prevalent among HD patients and is associated with female gender, lower educational level, elevated CRP, and higher parathyroid hormone levels. The severity of pain is particularly influenced by diabetes, low education level, and the number of painful body regions. Moreover, pain significantly impacts symptom burden and sleep quality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".