Two Year Reduction In Sleep Apnea Symptoms and Associated Diabetes Incidence After Weight Loss In Severe Obesity
Bibliographic record
Abstract
STUDY OBJECTIVES: To evaluate the effect of bariatric surgery on sleep apnea symptoms and obesity-associated morbidity in patients with severe obesity. DESIGN: Prospective study. SETTING: University hospitals and community centers in Sweden. INTERVENTION: We investigated the influence of weight loss surgery (n=1729) on sleep apnea symptoms and obesity-related morbidity using a conservatively treated group (n=1748) as a control. MEASUREMENTS AND RESULTS: Baseline BMI in surgical group (42.2+/-4.4 kg/m(2)) and control group (40.1+/-4.6 kg/m(2)) changed -9.7+/-5 kg/m(2) and 0+/-3 kg/m(2), respectively, at 2-year follow-up. In the surgery group, there was a marked improvement in all obstructive sleep apnea (OSA) symptoms compared with the control group (P <0.001). Persistence of snoring (21.6 vs 65.5%, adjusted OR 0.14, 95% CI 0.10-0.19) and apnea (27.9 vs 71.3%, adjusted OR 0.16, 95% I 0.10-0.23) were much less in the surgery group compared with controls. Compared with subjects with no observed apnea at follow-up (n=2453), subjects who continued to have or developed observed apnea (n=404) had a higher incidence of diabetes (adjusted OR 2.03, 95% CI 1.19-3.47) and hypertriglyceridemia (adjusted OR 1.86, 95% CI 1.07-3.25) but not hypertension (adjusted OR 1.09, 95% CI 0.65-1.83) or hypercholesterolemia (adjusted OR 0.91, 95% CI 0.53-1.58). CONCLUSION: Bariatric surgery results in a marked improvement in sleep apnea symptoms at 2 years. Despite adjustment for weight change and baseline central obesity, subjects reporting loss of OSA symptoms had a lower 2-year incidence of diabetes and hypertriglyceridemia. Improvement in OSA in patients losing weight may provide health benefits in addition to weight loss alone.
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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 | 0.001 |
| 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".