Nasal nitric oxide as a long‐term monitoring and prognostic biomarker of mucosal health in chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Nasal nitric oxide (nNO) is a potential biomarker of chronic rhinosinusitis (CRS), and correlates well with endoscopic and radiologic severity of disease. However, the long-term profile of nNO as a biomarker is not established in the literature. The objectives of our study were to examine whether nNO can maintain this correlation in a 5-year follow-up after endoscopic sinus surgery (ESS) and to investigate whether nNO value can be used to prognosticate revision rates in patients with CRS. METHODS: We enrolled CRS patients 5 years after initial ESS at our institution. Patients underwent initial ESS at our institution between January 2013 and January 2015. Patients prospectively had the following measurements at baseline, 1 month, 6 months, and 5 years post-ESS: nNO levels, Lund-Kennedy Endoscopy Score (LKES), and 22-item Sino-Nasal Outcome Test-22 (SNOT-22) score. We also compared the nNO levels between patients who underwent revision ESS and those who did not. RESULTS: There were 32 patients included in the study with 8 patients undergoing revision ESS during the 5-year follow-up. nNO levels were elevated at 1 month, 6 months, and 5 years post-ESS compared to baseline. A significant negative correlation between nNO and LKES was found at 5 years post-ESS. nNO levels were significantly reduced at baseline and 6 months post-ESS in the revision cohort compared to the nonrevision cohort despite having comparable radiologic severity. CONCLUSION: nNO may serve as a noninvasive long-term biomarker to monitor sinus disease severity and to prognosticate results in patients with CRS. This has implications for potential integration into clinical practice.
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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 | 0.001 | 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 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".