Surgery for Obstructive Sleep Apnea in Obese Children: Literature Review and Meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: Surgical intervention for obstructive sleep apnea (OSA) in overweight and obese children may not be as effective as it is in normal-weight children. The purpose of this study was to systematically review the effects of various surgical interventions for OSA in obese children and to meta-analyze the current data. DATA SOURCES: PubMed, OVID, and Cochrane databases. REVIEW METHODS: Databases were searched for studies examining adenotonsillectomy, uvulopalatopharyngoplasty, supraglottoplasty, or tongue base surgeries and combinations in obese children with OSA. Adenotonsillectomy was the only procedure with enough data for meta-analysis; polysomnographic data were extracted and analyzed using a random-effects model. RESULTS: For adenotonsillectomy, 11 studies were included in the meta-analysis. Despite significant improvement in the apnea-hypopnea index (22.9 to 8.1 events/h, P < .001), respiratory disturbance index (24.8 to 10.4 events/h, P < .001), and oxygen saturation nadir (78.4% to 87.0%, P < .001), rates of persistent OSA ranged from 51% to 66%, depending on the outcome criterion used. There was evidence of limited effectiveness for surgical interventions to treat OSA in obese children using uvulopalatoplasty (12.5%) and tongue base surgery (74%-88%). CONCLUSIONS: Surgical interventions for OSA in overweight and obese children are effective at reducing OSA but with higher rates of persistent OSA than reported for normal-weight children. However, the amount of reduction appears to vary by surgical procedure. More attention should be paid toward preoperative weight loss and patient selection, and parents should be provided with realistic postoperative expectations in this difficult-to-treat population.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".