<i>Reviews</i>: Diagnosis and Management of Insomnia in Dialysis Patients
Bibliographic record
Abstract
Sleep-related complaints affect 50-80% of patients on dialysis. Sleep disorders impair quality of life significantly. Increasing evidence suggests that sleep disruption has a profound impact both on an individual and on a societal level. The etiology of sleep disorders is often multifactorial: biologic, social, and psychological factors play a role. This is especially true for insomnia, which is the most common sleep disorder in different populations, including patients on dialysis. Biochemical and metabolic changes, lifestyle factors, depression, anxiety, and other underlying sleep disorders can all have an effect on the development and persistence of sleep disruption, leading to chronic insomnia. Insomnia is defined as difficulty initiating or maintaining sleep, or having nonrestorative sleep. It is also associated with daytime consequences, such as sleepiness and fatigue, and impaired daytime functioning. In most cases, the diagnosis of insomnia is based on the patient's history, but in some patients objective assessment of sleep pattern may be necessary. Optimally the treatment of insomnia involves the combination of both pharmacologic and nonpharmacologic approaches. In some cases acute insomnia resolves spontaneously, but if left untreated, it may lead to chronic sleep problems. The treatment of chronic insomnia is often challenging. There are only a few studies specifically addressing the management of this sleep disorder in patients with chronic renal disease. Considering the polypharmacy and altered metabolism in this patient population, treatment trials are clearly needed. This article reviews the diagnosis of sleep disorders with a focus on insomnia in patients on dialysis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".