Restless legs syndrome, insomnia and quality of life in patients on maintenance dialysis
Bibliographic record
Abstract
BACKGROUND: In a cross-sectional study, we analysed the complex relationship between restless legs syndrome (RLS), insomnia and specific insomnia symptoms and health-related quality of life (QoL) in patients on maintenance dialysis. METHODS: Data were obtained from 333 patients on chronic maintenance dialysis. To assess the prevalence of RLS, we used the RLS Questionnaire (RLSQ). The Athens Insomnia Scale (AIS) was used to assess insomnia and QoL was measured with the Kidney Disease Quality-of-Life Questionnaire. RESULTS: The prevalence of RLS was 14%. The number of comorbid conditions was significantly higher in patients with vs without RLS (median: three vs two; P<0.05). RLS patients were twice as likely to have significant insomnia as patients without RLS (35% vs 16%; P<0.05). Furthermore, RLS was associated with impaired overall sleep quality (median AIS score: 8 vs 4; P<0.01) and poorer QoL. RLS was a significant and independent predictor of several of the QoL domains after statistical adjustment for clinical and socio-demographic covariables. Importantly, this association remained significant even after adjusting for sleep quality. CONCLUSIONS: RLS is associated with poor sleep, increased odds for insomnia and impaired QoL in patients on maintenance dialysis. Based on the present results, we suggest that both sleep-related and sleep-independent factors may confer the effect of RLS on QoL.
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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 | 0.001 |
| 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".