Bariatric Surgery in the Treatment of Obstructive Sleep Apnea in Morbidly Obese Patients
Bibliographic record
Abstract
BACKGROUND: Weight loss has been shown effective in the treatment of the obstructive sleep apnea-hypopnea syndrome. Regrettably, many obese patients are unable to achieve sustained and useful weight loss by dietary means. Recently, bariatric surgery has emerged as an alternative to treat obesity and many of its comorbidities, although its role for sleep apnea treatment is still not defined. OBJECTIVES: To evaluate the impact of bariatric surgery on obstructive sleep apnea in morbidly obese patients. METHODS: In this cohort study, polysomnography, Epworth Sleepiness Scale questionnaire and clinical assessment were performed in 12 of 13 morbidly obese patients with moderate to severe obstructive sleep apnea treated with bariatric surgery through Roux-en-Y gastric bypass procedure after a minimum of 18 months post surgery. RESULTS: The mean (+/-SD) loss of excess body weight was 70.5 +/- 24%. The mean level obtained in the Epworth Scale was 4.8. There was a significant reduction in the apnea-hypopnea index, from a median of 46.5 (range: 33-140) to 16 (range: 0.9-87) events per hour (p < 0.05), an improvement in mean oxygen saturation from 85.7 +/- 5.1 to 94.5 +/- 3.6% (p < 0.05) and in minimum oxygen saturation from 64.7 +/- 13.4 to 78.7 +/- 13.7% (p < 0.05). The magnitude of the weight loss and the improvements in mean and minimum oxygen saturation were positively correlated, (r = 0.76; p <or= 0.05, and r = 0.59; p
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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".