<i>Reviews</i>: Diagnosis and Management of Sleep Apnea Syndrome and Restless Legs Syndrome in Dialysis Patients
Bibliographic record
Abstract
Sleep complaints are very common in patients with end-stage renal disease (ESRD) and contribute to their impaired quality of life. Both obstructive and central sleep apnea syndromes are reported more often in patients on dialysis than in the general population. Impaired daytime functioning, sleepiness, and fatigue, as well as cognitive problems, are well known in patients with sleep apnea. Increasing evidence supports the pathophysiological role of sleep apnea in cardiovascular disorders, which are the leading cause of death in ESRD patients. Uremic factors may be involved in the pathogenesis of sleep apnea in this patient population and optimal dialysis may reduce disease severity. Furthermore, treatment with continuous positive airway pressure may improve quality of life and may help to manage hypertension in these patients. Secondary restless legs syndrome is highly prevalent in patients on maintenance dialysis. The pathophysiology of the disorder may also involve uremia-related factors, iron deficiency, and anemia, but genetic and lifestyle factors might also play a role. The treatment of restless legs syndrome involves various pharmacologic approaches and might be challenging in severe cases. In this article we review the diagnosis and treatment of sleep apnea and restless legs syndrome, with a focus on dialysis patients. We also briefly review current data regarding sleep problems after transplantation, since these studies may indirectly shed light on the possible pathophysiological role of uremia or dialysis in the etiology of sleep disorders. Considering the importance of sleep disorders, more awareness among professionals involved in the care of patients on dialysis is necessary. Appropriate management of sleep disorders could improve the quality of life and possibly even impact upon survival of renal patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".