Sleep apnea in patients with chronic kidney disease: a single center experience
Bibliographic record
Abstract
PURPOSE: The primary objective of this cross-sectional study was to test factors associated with sleep apnea in patients with chronic kidney disease (CKD). The prevalence of sleep apnea was also assessed. METHODS: We recruited patients with CKD Stage 3-5 who lived in the St. John's area from September 2012 to December 2012. The Berlin Questionnaire and Short Form 36 Quality of Life Health Survey Questions (SF-36) were administered to all participants. RESULTS: We recruited 303 patients (41% female). A total of 157 (51.8%) patients had a high risk for sleep apnea. Higher body mass index and young age were correlated with sleep apnea. Physical component score of SF-36 (PCS) tested as a continuous variable indicated a significant association with the risk for sleep apnea (OR: 0.97, 95% CI: 0.94-0.99, p = 0.03). The association implies 3% change per one point increase in PCS. We categorized mental component score of SF-36 (MCS) into four quartiles, as the linearity assumption was violated. There was a 61% risk increase for poor sleep in those with an MCS score less than the 75th percentile, when compared to those above the 75th percentile (OR: 0.39, 95% CI: 0.21-0.71, p = 0.002). CONCLUSIONS: Sleep apnea is common in kidney patients. People who have low PCS and MCS scores are more prone to sleep apnea or vice versa. Our results also indicate that high BMI and young age are associated with sleep apnea.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".