Quality of life and metabolic disorders in patients with obstructive sleep apnea
Bibliographic record
Abstract
PURPOSE: Quality of life (QoL) may be poor in patients with sleep apnea depending on multifactorial reasons. In this observational study, we examined the factors determining QoL in patients with obstructive sleep apnea (OSA) and nonapneic snoring subjects. METHODS: Complete assessments were obtained on 111 subjects who diagnosed OSA and 18 nonapneic snoring subjects. Fasting blood samples of all of subjects were taken to determine insulin resistance (IR) and oral glucose tolerance tests were performed to diagnose disorders of glucose metabolism (DGM). Quality of life, with short form (SF)-36, and excessive daytime sleepiness, with epwort sleepness scale (ESS), were evaluated. RESULTS: The mean age of the patients with OSA was higher than that of the nonapneic snoring subjects (48.4 ± 9.6 years and 43.0 ± 11.8 years, respectively; p=0.03). BMI was also significantly higher in the patients with OSA than in the nonapneic snorers (31.0 ± 4.5 and 27.1 ± 4.0, respectively; p=0.001). The mental health component in the patients with OSA was slightly but not significantly lower than the nonapneic snoring subjects (p=0.05). A negative correlation among most domains of quality of life with scores of ESS, body mass index (BMI), presence of hypertension (HT) and DGM was found. Only physicial functioning was negatively correlated with apnea hypopnea index (AHI). In linear regression analysis, there were negative associations among physical functioning with BMI, presence of HT and DGM while there was no association between physicial functioning and AHI. In addition, there were negative correlations between mental health component with BMI and presence of HT in the multivariate analysis. Obese patients with OSA had lower physicial and mental components compared with nonobese patients with OSA. CONCLUSION: The impact of OSA on quality of life can be attributed to excessive daytime sleepiness. Obesity and metabolic disorders in patients with OSA may also negatively affect the quality of life.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".