Sino‐Nasal outcome test‐22 outcomes after sinus surgery: A systematic review and meta‐analysis
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Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The goal of the study was to perform a systematic review with meta-analysis to determine the mean change in the 22-item Sino-Nasal Outcome Test (SNOT-22) across patients who have had endoscopic sinus surgery (ESS) for chronic rhinosinusitis (CRS) in the literature. METHODS: A literature search was performed to identify studies that assessed SNOT-22 scores before and after ESS in adult patients with CRS. A random effects model with inverse variance weighting was used to generate the mean change after surgery, along with the forest plot and 95% confidence interval (CI). The impact of patient-specific factors across studies was assessed using a mixed-effects meta-regression. RESULTS: The final study list included 40 unique patient cohorts published from 2008 to 2016. All studies showed a statistically significant change in mean SNOT-22 scores between baseline and postoperative time points (P < .001), ranging from 12.7 to 44.8, at an average follow-up of 10.6 months. The summary change in mean SNOT-22 across all studies was 24.4 (95% CI: 22.0-26.8). After forward, step-wise multivariate modeling, studies with higher mean preoperative SNOT-22 score and higher asthma prevalence were associated with greater changes in SNOT-22 score after ESS, whereas studies with longer mean follow-up had smaller changes in SNOT-22 score. CONCLUSIONS: Studies evaluating quality-of-life outcomes after sinus surgery using the SNOT-22 instrument universally show significant improvement after ESS. Across the published literature, the magnitude of change is quite variable and appears to be influenced by a number of factors including baseline SNOT-22 score, asthma prevalence, and length of follow-up. Laryngoscope, 128:581-592, 2018.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.006 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 it