Prevalence of Knee Pain and Its Relation to Depression, Anxiety, and Health-Related Quality of Life Among Maintenance Hemodialysis Patients
Bibliographic record
Abstract
Background/Objectives: Knee pain in hemodialysis (HD) patients might affect health-related quality of life (HRQoL) and may be related to anxiety and depressive symptoms. The aim of this study was to assess the prevalence of knee pain in chronic HD patients and to determine its relationship with anxiety, depression, and HRQoL, Methods: This multicenter cross-sectional study was carried out on chronic HD patients. Sociodemographic, clinical, and therapeutic data were collected. The Knee Pain Screening Tool (KNEST) was used to screen for knee pain. Patients with knee pain were instructed to complete the visual analog scale (VAS) for pain and the Western Ontario and McMaster Universities Arthritis Index (WOMAC). The patients also completed an Arabic-language version of the Hospital Anxiety and Depression Scale (HADS) and the Kidney Disease Quality of Life-36 (KDQOL-36™) questionnaire. Results: This study included 271 chronic HD patients; the median age was 51 (IQR 21) years, and most of them were males (59%). Of them, 158 had knee pain. Those with knee pain were more likely to have anxiety compared to those without (p = 0.002) and significantly lower scores on the symptom/problem (p = 0.03) and burden of kidney disease domains (p = 0.047) and the physical health (p < 0.001) and mental health components (p = 0.001). Furthermore, those with moderate to severe knee pain were more likely to experience anxiety (p = 0.001) and depression (p = 0.005) and have a lower physical health composite (PHC) than those with mild knee pain (p = 0.046). Conclusions: HD patients have a significant prevalence of knee pain that is usually associated with anxiety and leads to worse HRQoL than those without knee pain.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".