Sino‐Nasal outcome test‐22 outcomes after sinus surgery: A systematic review and meta‐analysis
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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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.043 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".