Sleep quality and its correlates in patients with chronic kidney disease: a cross-sectional design
Bibliographic record
Abstract
PURPOSE: Since sympathovagal imbalance influences clinical phenomena, such as hypertension, diabetes mellitus, chronic kidney disease (CKD) and sleeping problems, there should be correlations between these conditions. We hypothesized that sleep quality would be correlated with estimated glomerular filtration rate (eGFR), blood pressure and the presence of diabetes. METHODS: We included 303 CKD patients in this study. We employed the Pittsburgh Sleep Quality Index (PSQI), Beck Depression Inventory (BDI) and Short Form 36 Quality of Life Health Survey Questions (SF-36) to screen sleeping disturbances, depression and quality of life, respectively. A chart review was performed for the patients' demographics, diagnoses and certain laboratory parameters--including blood glucose, hemoglobin, albumin, calcium, phosphate, parathyroid hormone and eGFR. Multivariate logistic regression models were employed to estimate odds ratios with 95% confidence intervals. RESULTS: We included 303 patients in this cross-sectional study. A total of 101 patients were on dialysis. In the univariate models, gender, calcium and mental component summary scores (MCS) reached a significant level of 0.1, and those covariates were included in the multivariate analysis. The reduced models included gender and MCS categories. Female gender increases the risk for poor sleep quality. In our report, evidence suggests MCS domain scores are inversely related to the risk for impaired sleep. CONCLUSION: Our results indicated a high burden of sleep disturbances in kidney patients. In addition, female gender and having low MCS scores may influence sleep quality in kidney 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".