Quality of life among untreated sleep apnea patients compared with the general population and changes after treatment with positive airway pressure
Bibliographic record
Abstract
Obstructive sleep apnea leads to recurrent arousals from sleep, oxygen desaturations, daytime sleepiness and fatigue. This can have an adverse impact on quality of life. The aims of this study were to compare: (i) quality of life between the general population and untreated patients with obstructive sleep apnea; and (ii) changes of quality of life among patients with obstructive sleep apnea after 2 years of positive airway pressure treatment between adherent patients and non-users. Propensity score methodologies were used in order to minimize selection bias and strengthen causal inferences. The enrolled obstructive sleep apnea subjects (n = 822) were newly diagnosed with moderate to severe obstructive sleep apnea who were starting positive airway pressure treatment, and the general population subjects (n = 742) were randomly selected Icelanders. The Short Form 12 was used to measure quality of life. Untreated patients with obstructive sleep apnea had a worse quality of life when compared with the general population. This effect remained significant after using propensity scores to select samples, balanced with regard to age, body mass index, gender, smoking, diabetes, hypertension and cardiovascular disease. We did not find significant overall differences between full and non-users of positive airway pressure in improvement of quality of life from baseline to follow-up. However, there was a trend towards more improvement in physical quality of life for positive airway pressure-adherent patients, and the most obese subjects improved their physical quality of life more. The results suggest that co-morbidities of obstructive sleep apnea, such as obesity, insomnia and daytime sleepiness, have a great effect on life qualities and need to be taken into account and addressed with additional interventions.
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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) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".