Does nasal surgery improve multilevel surgical outcome in obstructive sleep apnea<scp>:</scp> A multicenter study on 735 patients
Bibliographic record
Abstract
Abstract Objective Does nasal surgery affect multilevel surgical success outcome. Methods Prospective eight country nonrandomized trial of 735 obstructive sleep apnea (OSA) patients, who had multilevel palate and/or tongue surgery, divided into two groups, with or without nose surgery. Results There were 575 patients in nose group, 160 patients in no nose group. The mean age for nose group 44.6 ± 11.4, no nose group 44.2 ± 11.8. Mean preoperative BMI for nose group 27.5 ± 3.6, no nose group 27.5 ± 4.1, mean postoperative BMI nose group 26.3 ± 3.7, no nose group 27.1 ± 3.8 (P = .006). Mean preoperative AHI nose group 32.7 ± 19.4, no nose group 34.3 ± 25.0 (P = .377); and mean postoperative AHI nose group 13.5 ± 10.2, no nose group 17.1 ± 16.0 (P = .001). Mean preoperative ESS nose group was 11.3 ± 4.7, no nose group was 10.4 ± 5.4 (P = .051); and mean postoperative ESS nose group was 5.3 ± 3.2, no nose group was 6.7 ± 2.8 (P = .001). The nose group had higher percentage change (adjusted for age, gender, BMI) in AHI (33.7%, 95% CI 14% to 53.5%) compared to the no nose group (P = .001); the nose group also had more percentage change in ESS (37%, 95% CI 23.6% to 50.3%) compared to the no nose group (P < .001). Change in BMI did not affect AHI nor ESS change (Cohen effect 0.03 and 0.14, respectively). AHI change in both groups were also statistically significant in the mild OSA (P = .008) and the severe OSA (P = .01). Success rate of surgery for the nose group 68.2%, while the no nose group 55.0% (P = .002). Conclusion Combining nose surgery in multilevel surgery improves surgical success. Level of evidence IIC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".