Corrective Nasal Surgery after Maxillomandibular Advancement for Obstructive Sleep Apnea: Experience from 379 Cases
Bibliographic record
Abstract
Objective Efficacy of maxillomandibular advancement (MMA) in the treatment of obstructive sleep apnea (OSA) is associated with degree of maxillary advancement. Large maxillary advancement leads to profound changes of the nasolabial region. We present the incidence and indications of post-MMA corrective nasal surgery in a large cohort. Study Design Case series with chart review. Setting University medical center. Subjects and Methods A total of 379 subjects with OSA underwent MMA at Stanford Hospital (surgeons: S.Y.L., R.W.R.) from August 1992 to December 2015. Data were collected on age, sex, American Society of Anesthesiologists score, polysomnography parameters, and history of nasal surgery. Primary outcome parameters were the incidence and indications of post-MMA corrective nasal surgery. Results Of 379 subjects, the surgical success rate was 76.3% based on the change in respiratory disturbance index. Seventy-one subjects (18.7%) underwent corrective nasal surgery after MMA, whereas 48 underwent functional nasal surgery and 23 underwent both functional and aesthetic nasal surgery. Lower oxygen saturation nadir and higher baseline respiratory disturbance index were associated with increased likelihood of post-MMA corrective nasal surgery. Conclusion MMA surgical success is associated with degree of maxillary advancement, which is especially significant in patients with severe OSA. Patients must be counseled on its impact in nasal function and aesthetics. Our series, the largest to date to address this question, suggests that the incidence of post-MMA corrective nasal surgery is at least 18.7%. Prospectively, refinement in MMA techniques is needed to minimize postoperative compromise in nasal form and function.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".