Quality of sleep and health-related quality of life in haemodialysis patients
Bibliographic record
Abstract
BACKGROUND: Sleep complaints are common in haemodialysis patients. In the general population, insomnia impacts negatively on health-related quality of life (HRQoL). The objective of this study was to examine the association between quality of sleep and HRQoL in haemodialysis patients independent of known predictors of HRQoL. METHODS: Quality of sleep was measured using the Pittsburgh Sleep Quality Index (PSQI) and HRQoL was measured using the Medical Outcomes Study 36-item Short Form (SF-36) in 89 haemodialysis patients. RESULTS: Sixty-three (71%) subjects were 'poor sleepers' (global PSQI >5). The SF-36 mental component summary (MCS) and physical component summary (PCS) correlated inversely with the global PSQI score (MCS, r = -0.28, P < 0.01; PCS, r = -0.45, P < 0.01). The PCS score also correlated with age (r = -0.24, P = 0.02), haemoglobin (r = 0.21, P = 0.048) and comorbidity (r = -0.40, P < 0.01), and mean PCS was lower in depressed subjects (26.2 vs 35.9, P = 0.02). Subjects with global PSQI >5 had a higher prevalence of depression, lower haemoglobin and lower HRQoL in all SF-36 domains. The global PSQI score was a significant independent predictor of the MCS and PCS after controlling for age, sex, haemoglobin, serum albumin, comorbidity and depression in multivariate analysis. CONCLUSIONS: Poor sleep is common in dialysis patients and is associated with lower HRQoL. We hypothesize that end-stage renal disease directly influences quality of sleep, which in turn impacts on HRQoL.
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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 | 0.003 |
| 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) | 0.001 | 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".