A Comprehensive Assessment of Pain Among Haemodialysis Patients: A Cross-sectional Study
Bibliographic record
Abstract
Objectives Pain is a prevalent and significant concern among haemodialysis (HD) patients, with most experiencing moderate to severe intensity. It has multiple detrimental effects on patients' quality of life and overall well-being. Therefore, regular and systematic pain assessment is crucial for early detection and effective management. The study aimed to assess the prevalence, severity, and location of pain among patients undergoing maintenance HD using the McGill Pain questionnaire (MPQ), and to determine the association between pain and selected socio-demographic and clinical variables. Material and Methods The cross-sectional study was conducted in the dialysis unit of a tertiary care centre in northern Kerala. HD patients (n=110) aged ≥18 years were enrolled. Patients with cognitive impairments like dementia, delirium, or critical illnesses were excluded from the study. The structured questionnaire for socio-demographic and clinical variables and the Short-Form MPQ (SF-MPQ) for pain assessment were used. Results The majority of participants (92%) reported experiencing varying degrees of pain. Many (39%) experienced pain following dialysis treatment, whereas many (44%) reported pain during the procedure. Chronic pain was reported by 9%. A higher percentage (70%) reported pain at the fistula site during cannulation, and 60% experienced pain in the lower extremities. The total pain rating index (PRI) was calculated as 4.8, while the mean Present Pain Intensity (PPI) was moderate (3.9 ± 1.88). Sensory subscale analysis revealed that 64.5% of respondents described pain as cramping and 48.2% identified it as aching. In the affective subscale, 59% of participants reported their pain as tiring and exhausting. Half of the participants reported that pain significantly interfered with their daily functioning. A significant association was observed between pain and variables such as age, gender, and duration of being on HD. Conclusion A high prevalence of pain was observed among HD patients, significantly impacting their functional ability. Pain assessment aids in identifying complications, such as musculoskeletal and neurological problems and allows for tailored interventions. The care team needs to assess pain symptoms consistently and manage them effectively. Proactive pain management helps to improve quality of life, enhance daily functioning, reduce complications, and promote overall well-being.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".