Sleep apnea in hemodialysis patients: Risk factors and effect on survival
Bibliographic record
Abstract
UNLABELLED: Sleep disorders are common in patients with end-stage renal disease (ESRD). Using a simple questionnaire, we estimate the probability of sleep apnea in ESRD patients, determine the factors associated with a higher probability of sleep apnea, and determine the association between the probability of sleep apnea and cardiovascular and all-cause mortality. STUDY DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: prevalent hemodialysis patients (n=270) in 7 urban outpatient hemodialysis units. PREDICTOR: Probability of sleep apnea as quantified by the Flemons questionnaire. OUTCOMES AND MEASUREMENTS: Clinical, demographic, and dialysis-related characteristics were obtained at baseline. Total and cardiovascular mortality was ascertained after a median follow-up of 34 months. The probability of sleep apnea was low in 79 (29%) patients, moderate in 116 (43%) patients, and high in 75 (28%) patients. Male gender (odds ratio [OR] 5.13, p<0.001), obesity (BMI >30, OR 7.58, p<0.01), and interdialytic weight gain (OR 1.72/kg change, p<0.004) were independently associated with a high probability of sleep apnea. A high probability of sleep apnea at baseline did not predict total (hazard ratio [HR] 0.81, p=NS) or cardiovascular mortality (HR 0.9, p=NS). The Flemons questionnaire is validated in the general population, but has not been tested specifically in hemodialysis patients. The study may not be adequately powered to detect a difference in mortality. A high proportion of hemodialysis patients are likely to have sleep apnea; a simple bedside questionnaire can be used for screening to identify these patients. Excessive interdialytic weight gain is a potentially modifiable factor that increases the likelihood of sleep apnea. Despite the presence of a strong association between sleep apnea and mortality in the general population, a similar association could not be demonstrated in ESRD patients with a high prevalence of this condition.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".