Nasal nitric oxide as a marker of sinus mucosal health in patients with nasal polyposis
Bibliographic record
Abstract
BACKGROUND: Reduced nasal nitric oxide (nNO) has been shown in patients with chronic rhinosinusitis (CRS) but its clinical significance remains uncertain. The objective of this study was to measure nNO changes in patients undergoing endoscopic sinus surgery (ESS) for CRS and to explore its relationship to clinical measures of sinus mucosal health postoperatively. METHODS: This was a prospective study of CRS patients undergoing ESS. Patients had the following measurements at baseline and at 1 and 6 months post-ESS: nNO levels, Lund-Kennedy Endoscopy Score (LKES), and 22-item Sino-Nasal Outcome Test (SNOT-22) score. Statistical analysis was performed using GraphPad Prism 6. RESULTS: Thirty-nine patients were enrolled, of these 84.6% had CRS with nasal polyps. Baseline Lund-Mackay computed tomography (CT) score was 16.9 ± 5.1. There was a statistically significant increase in nNO levels from baseline to 1 month and 6 months postoperatively (p < 0.0001). The SNOT-22 and LKES followed a similar trend with a significant and sustained improvement at 1 month and 6 months post-ESS (p < 0.0001). Subgroup analysis revealed that changes in nNO were driven by the polyp cohort because nonpolyp patients had no significant changes in their nNO postoperatively. No correlation was found between nNO levels and SNOT-22. However, a significant negative correlation was found between nNO and LKES (p < 0.0001), suggesting healthier sinus mucosa was associated with higher nNO levels. CONCLUSION: This is the first study to show that nNO levels may be a marker of sinus mucosal health following ESS in patients with polyps. This has important implications for nNO in its potential etiologic role in mediating ongoing sinus inflammation.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".