Longitudinal improvement and stability of the SNOT‐22 survey in the evaluation of surgical management for chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Patients with chronic rhinosinusitis (CRS) have significant quality-of-life (QOL) improvements following endoscopic sinus surgery (ESS). These improvements remain stable and persist between 6 months and 20 months as measured by the Rhinosinusitis Disability Index and the Chronic Sinusitis Survey. There has yet to be an evaluation of the longitudinal stability of the 22-item Sino-Nasal Outcome Test (SNOT-22) after ESS in patients with CRS. METHODS: Adults with medically recalcitrant CRS who were considered surgical candidates were enrolled in a prospective, multicenter, observational cohort study from February 2011 to February 2013. Baseline evaluation of subjects included assessment of clinical characteristics, measures of CRS-specific disease severity, and QOL evaluation using the SNOT-22. Subjects were then re-evaluated at approximately 6-month, 12-month, and 18-month intervals postoperatively. Data was analyzed using repeated measures analysis of variance (ANOVA) with Bonferroni corrections for matched pairwise comparisons. RESULTS: A total of 110 patients completed baseline evaluations and follow-up for all 3 postoperative time points. Significant improvement in SNOT-22 scores was seen between baseline and 6 months across both SNOT-22 total and subdomain scores (p < 0.001). There was no statistically significant difference between the 6-month, 12-month, and 18-month time points in the total SNOT-22 score or its domains (p ≥ 0.125) for both the entire cohort or subgroups (p ≥ 0.077). CONCLUSION: Postoperative improvement in CRS-specific QOL and symptom severity, as measured by the SNOT-22, suggest stability and durability between 6 months and 18 months. Further study on the longitudinal stability of the SNOT-22 past the 18-month time frame will help further refine clinical study of CRS and provide further understanding of temporal improvements following ESS.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".