Redefining the timing of surgery for obstructive sleep apnea in anatomically favorable patients
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Healthcare remunerating agencies in North America require patients with obstructive sleep apnea (OSA) to undergo a continuous positive airway pressure (CPAP) trial before funding surgical therapy. The adherence rate of CPAP is problematic. This study's objective was to determine the proportion of surgically favorable patients who failed CPAP who subsequently benefitted from surgical therapy, and to explore consideration of surgical therapy as first-line treatment in this specific OSA subpopulation. STUDY DESIGN: This was a prospective cohort study. METHODS: Patients with moderate-severe OSA who had failed a minimum 6-month trial of CPAP were recruited. All had optimal anatomy for surgery and underwent tonsillectomy with palatoplasty ± septoplasty. Outcome measures included apnea-hypopnea index (AHI), Epworth Sleepiness Scale (ESS), and Sleep Apnea Quality of Life Index (SAQLI-E), and blood pressure. Patients were followed for 1 year. RESULTS: By AHI measurement, 85.7% of patients in the entire cohort were successfully treated by surgery. ESS while on CPAP was 13.7 ± 2.9, improving to 4.1 ± 2.5 after surgery. SAQLI-E scores on CPAP were 25.7 ± 5.8, improving to 10.2 ± 3.2 after surgery. Blood pressure remained elevated during CPAP but normalized after surgery. All changes were significant at P < .001. CONCLUSIONS: Surgical intervention improved OSA severity as measured by the ESS, SAQLI-E, and blood pressure. These measures had not improved on CPAP. AHI improved as well. Our results suggest that certain patients with OSA may be managed more effectively with surgery than CPAP, without confounding issues of treatment adherence and with only minor surgical risk. LEVEL OF EVIDENCE: 2 Laryngoscope 124:S1-S9, 2014.
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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 | 0.004 |
| 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".