The effect of nasal surgery on nasal continuous positive airway pressure compliance
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Nasal continuous positive airway pressure (CPAP) is the standard therapy for sleep apnea; however, compliance rates are historically poor. Among the most commonly cited reasons for nonadherence is nasal obstruction. Our study sought to examine if nasal surgery actually increases CPAP compliance. STUDY DESIGN: Prospective case series. METHODS: Nasal CPAP-intolerant obstructive sleep apnea (OSA) patients, with documented nasal obstruction, underwent septoplasty plus inferior turbinoplasty. Preoperative and postoperative data were collected on CPAP usage per night and subjective nasal obstruction with the Nasal Obstruction Symptom Evaluation (NOSE) Scale questionnaire. RESULTS: Eighteen patients met inclusion criteria and underwent septoplasty. CPAP usage increased significantly from 0.5 hours per night preoperatively to 5 hours per night postoperatively (P < .05). Subjective nasal obstruction on the NOSE Scale decreased from 16.1 preoperatively to 5.4 following surgical intervention (P < .05). CPAP pressure decreased from 11.9 preoperatively to 9.2 after surgery, with a trend toward significance (P = .062). CONCLUSIONS: This study demonstrates improved CPAP compliance rates following septoplasty in OSA patients with nasal obstruction. Correction of nasal obstruction should be offered in nasal CPAP-intolerant individuals to improve CPAP compliance.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".