Bibliographic record
Abstract
PURPOSE OF REVIEW: To provide an update on the prevalence and clinical importance of sleep disorders in patients with end-stage renal disease (ESRD) and to highlight recent findings on their pathogenesis and treatment. RECENT FINDINGS: Although poor sleep quality is common in patients with ESRD, it is underrecognized. In addition to causing impaired quality of life, poor sleep is associated with increased cardiovascular mortality. There is evidence that sleep quality may be improved by cognitive-behavioral therapy that may help to reduce the frequent use of hypnotic medication. There are differences in the clinical presentation of sleep apnea in patients with ESRD compared with patients with normal renal function. The pathogenesis of sleep apnea in patients with ESRD appears to be related both to increased chemosensitivity, which destabilizes the control of breathing, and narrowing of the upper airway, which predisposes to closure of the airway during sleep. Although renal transplantation corrects periodic leg movements, it does not always correct sleep apnea. SUMMARY: Sleep disorders are common in patients with ESRD. Although our understanding of their pathogenesis and clinical presentation has grown in recent years, further research is required to determine their impact on clinical outcomes in this patient population.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".