Pain, Sleep Disturbance, and Quality of Life in Patients with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Few studies have assessed sleep disturbances or perception of pain in patients with early-stage chronic kidney disease. It was hypothesized that perception of pain and sleep disturbance would increase with chronic kidney disease stage, that pain and sleep disturbance would correlate with psychosocial variables, and that there would be a higher prevalence of pain and sleep disturbances in patients with chronic kidney disease compared with general medical patients. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A total of 92 predialysis patients with chronic kidney disease and 61 general medical outpatients were evaluated using the Beck Depression Inventory, Illness Effects Questionnaire, Multidimensional Scale of Perceived Social Support, Satisfaction with Life Scale, Karnofsky Scale, Pittsburgh Sleep Questionnaire, and McGill Pain questionnaire. RESULTS: With the exception of expected differences in serum creatinine, estimated GFR, Karnofsky score, albumin, and hemoglobin, there were no significant differences between groups. A total of 69% of patients with chronic kidney disease experienced pain; 55.2% had disordered sleep. Pain was associated with quality-of-life indicators, including depression, burden of illness, and life satisfaction. Disordered sleep correlated with depression, illness burden, social support, and pain frequency. There were no differences in perception of pain or sleep disturbance between patients with chronic kidney disease and control patients. CONCLUSIONS: Pain is common in patients with early-stage chronic kidney disease and is associated with patients' perception of lower quality of life. The prevalence of pain, sleep disturbance, and abnormal psychologic status of patients with chronic kidney disease may be similar to outpatients with other chronic medical illnesses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 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".