Bibliographic record
Abstract
BACKGROUND: Functional endoscopic sinus surgery (FESS) is standard for patients who fail medical management of chronic sinusitis (CRS). The beneficial impact of surgery on CRS is well known. However, patients often note that their sleep is improved after FESS even without simultaneous correction of nasal obstruction. Sleep outcomes after FESS are significantly understudied. Hence in the current study we look to characterize patient sleep quality following sinus surgery. METHODS: Data was gathered from 2 sites (Western University [Canada] and the Asia Sleep Center [Singapore]). Patients meeting diagnostic criteria for CRS without nasal polyposis (CRSsNP) were included. Cases with polyposis and those who needed a septoplasty were excluded so as to purely analyze the impact of the sinus surgery on sleep. Sleep outcomes recorded at baseline just prior to surgery and 6 months after surgery were the Epworth Sleepiness Scale (EpSS) and the Pittsburgh Sleep Quality Index (PSQI). We also recorded 22-item Sino-Nasal Outcome Test (SNOT-22) scores and Nasal Obstruction Symptom Evaluation (NOSE) scores. Comparisons were made with paired t tests. RESULTS: Fifty-three patients met inclusion/exclusion criteria. Sleep outcomes showed a clinically and statistically significant improvement (EpSS before FESS = 14.7 ± 3.1, EpSS after FESS = 9.1 ± 1.1, p < 0.01; PSQI before FESS = 10.9 ± 2.8, PSQI after FESS = 5.3 ± 2.2, p < 0.01). CRS-specific outcomes were improved as well. Nasal obstruction scores did not change significantly. CONCLUSION: FESS improved sleep outcomes for the patients in our study. This was independent of correction of nasal obstruction. Sinus surgery for CRSsNP has a beneficial impact on sleep; this novel information can be used during patient counseling and for justification to third-party payers.
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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 | 0.001 |
| 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".