The predictive value of the preoperative Sinonasal outcome test‐22 score in patients undergoing endoscopic sinus surgery for chronic rhinosinusitis
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: With the aim of facilitating preference-sensitive decision making regarding elective endoscopic sinus surgery (ESS) for chronic rhinosinusitis (CRS), we set out to evaluate the predictive value of the 22-item Sinonasal Outcome Test (SNOT-22) patient-reported outcome measure and to compare outcomes of a UK cohort with a similar United States/Canadian-based study. STUDY DESIGN: Prospective observational cohort study, METHODS: Patients electing ESS in 87 UK hospitals were enrolled. The primary outcome was change in SNOT-22 score 3 months after surgery. Patients were categorized according to baseline SNOT-22 score, and the proportion of patients achieving a SNOT-22 minimal clinically important difference (MCID) of 8.9 was calculated, as well as the percentage change in SNOT-22 score. RESULTS: A total of 2,263 patients were included within this study. There was an average 40% reduction in SNOT-22 scores following surgery, and 66% of patients overall achieved the MCID. The proportion of patients achieving the MCID increased significantly with increasing baseline SNOT-22. Patients with a preoperative score of <20 failed to achieve a mean improvement greater than the MCID. Patients with a score of >30 had a greater than 70% chance of achieving the MCID. CRS patients with polyps had greater improvement than patients with CRS without polyps. The predictive value of the SNOT-22 is similar in the UK cohort, although overall patients did not benefit from surgery as much as their North American counterparts. CONCLUSIONS: Medically recalcitrant patients with CRS considering surgery should make decisions guided by their preoperative quality-of-life impairment, as measured by the SNOT-22. LEVEL OF EVIDENCE: 2b
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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 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".