Impact of Sleep Quality on Cardiovascular Outcomes in Hemodialysis Patients: Results from the Frequent Hemodialysis Network Study
Bibliographic record
Abstract
BACKGROUND: Poor sleep quality is a common, persistent, and important problem to patients with end-stage renal disease (ESRD). This report examines whether sleep quality is associated with dialysis treatment factors and other modifiable clinical factors in a large group of hemodialysis (HD) patients. METHODS: Cross-sectional analyses were conducted on baseline data collected from participants in the Frequent Hemodialysis Network trials. Sleep quality was measured using the Medical Outcomes Study Sleep Problems Index II (SPI II), a 9-item measure of sleep quality with higher scores reflecting poorer sleep quality. RESULTS: The participants had an age of 51.2 ± 13.6 years, 61% were male, 38% were black, and 42% had diabetes. Higher pre-dialysis serum phosphorus (per 0.5 mg/ml) (OR 0.91; 95% CI 0.85, 0.96) and depression (OR 0.16; 95% CI 0.10, 0.25) were independently associated with decrements in sleep quality. There was also a difference in time to recovery from dialysis for the fourth versus the first SPI II quartile (5.1 h; p < 0.0001). CONCLUSION: These findings underscore the link between sleep and daytime function and suggest that improving sleep may provide an opportunity to improve outcomes in ESRD. Whether sleep problems may be improved by reduction of serum phosphorus or treatment of depression in the HD population merits further investigation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".