Quality‐of‐Life Outcomes Following Endoscopic Resection of Sinonasal Inverted Papilloma
Bibliographic record
Abstract
OBJECTIVES: There is growing interest in assessing patient quality of life (QOL) following treatment of sinonasal tumors, including inverted papilloma (IP). We aimed to elucidate the natural history of postoperative QOL outcomes in IP patients treated with surgery. METHODS: Cases of sinonasal IP treated surgically at 4 tertiary academic rhinology centers were retrospectively reviewed. SNOT-22 scores were used to evaluate QOL preoperatively and postoperatively (1, 3, 6, 12 months). Repeated-measures ANOVA assessed for differences in mean scores over time. Linear regression identified factors associated with QOL longitudinally. RESULTS: 373 patients were analyzed. Mean preoperative SNOT-22 score was 20.6 ± 20.4, which decreased to 16.3 ± 18.8 (p = 0.041) and 11.8 ± 15.0 (p < 0.001) at 1 and 3 months postoperatively, respectively. No further changes in SNOT-22 scores occurred beyond 3 months postoperatively (p > 0.05). When analyzed by SNOT-22 subdomains, nasal, sleep, and otologic/facial subdomain scores (all p < 0.05) demonstrated improvement at 12-month follow-up compared with preoperative scores; this was not observed for the emotional subdomain score (p = 0.800). Recurrent cases were associated with higher long-term SNOT-22 scores (β = 7.08; p = 0.017). Age, sex, degree of dysplasia, prior surgery, primary site, and smoking history did not correlate with symptoms (all p > 0.05). CONCLUSIONS: QOL outcomes related to IP resection are largely driven by nasal, sleep, and otologic/facial subdomains, though patients appear to experience enduring improvement as early as 3 months postoperatively. Recurrent disease is a major driver of negative QOL. LEVEL OF EVIDENCE: 4 Laryngoscope, 135:579-585, 2025.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".