SNOT‐22 quality of life domains differentially predict treatment modality selection in chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Prior study demonstrated that baseline 22-item Sino-Nasal Outcome Test (SNOT-22) aggregate scores accurately predict selection of surgical intervention in patients with chronic rhinosinusitis (CRS). Factor analysis of the SNOT-22 survey has identified five distinct domains that are differentially impacted by endoscopic sinus surgery (ESS). This study sought to quantify SNOT-22 domains in patient cohorts electing both surgical or medical management and postinterventional change in these domains. METHODS: CRS patients were prospectively enrolled into a multi-institutional, observational cohort study. Subjects elected continued medical management or ESS. SNOT-22 domain scores at baseline were compared between treatment cohorts. Postintervention domain score changes were evaluated in subjects with at least six-month follow-up. RESULTS: A total of 363 subjects were enrolled with 72 (19.8%) electing continued medical management, whereas 291 (80.2%) elected ESS. Baseline SNOT-22 domain scores were comparable between treatment cohorts in sinus-specific domains (rhinologic, extranasal rhinologic, and ear/facial symptoms; p > 0.050); however, the surgical cohort reported significantly higher psychological (mean ± standard deviation [SD]: 16.0 ± 8.4 vs 12.0 ± 7.1; p < 0.001) and sleep dysfunction (13.7 ± 6.8 vs 10.5 ± 6.2; p < 0.001) than the medical cohort. Effect sizes for ESS varied across domains with rhinologic and extranasal rhinologic symptoms experiencing the greatest gains (1.067 and 0.997, respectively), whereas psychological and sleep dysfunction experiencing the smallest improvements (0.805 and 0.818, respectively). Patients experienced greater mean improvements after ESS in all domains compared to medical management (p < 0.001). CONCLUSION: Subjects electing ESS report higher sleep and psychological dysfunction compared to medical management but have comparable sinus-specific symptoms. Subjects undergoing ESS experience greater gains compared to medical management across all domains; however, these gains are smallest in the psychological and sleep domains.
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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 | 0.002 |
| 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 | 0.000 |
| 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".