Predictive Value of Nasal Nitric Oxide for Diagnosing Eosinophilic Chronic Rhinosinusitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
ObjectivesThe primary aim of this study was to assess disparities in nasal nitric oxide (NO) levels between individuals diagnosed with eosinophilic chronic rhinosinusitis (ECRS) and those without ECRS. The second aim was to ascertain the comparative predictive efficacy of these nasal NO levels for the presence of ECRS.MethodsA systematic analysis was conducted on relevant studies that compared nasal NO levels in individuals with ECRS and those without. Furthermore, the discriminatory capacity of nasal NO in distinguishing ECRS from non-ECRS cohorts was quantified. The risk of bias across studies was evaluated utilizing the Newcastle-Ottawa scale.ResultsThe comprehensive review encompassed a total of 5 studies involving 470 participants. Findings revealed that patients diagnosed with ECRS exhibited significantly higher levels of nasal NO, as measured in parts per billion (ppb), compared to their non-ECRS patients. The mean difference was 130.03 ppb (95% confidence interval: [66.30, 193.75], I2 = 58.7%). The diagnostic odds ratio for nasal NO in identifying ECRS was 9.29 ([5.85, 14.75], I2 = 26.4%). The area under the summary receiver operating characteristic curve was 0.82. The correlation between sensitivity and false positive rate was 0.53, suggesting a lack of heterogeneity. Sensitivity, specificity, negative predictive value, and positive predictive value were 69% ([0.55, 0.79], I2 = 77.0%), 83% ([0.73, 0.90], I = 68.5%), 77% ([0.69, 0.83], I = 50.1%), and 75% ([0.67, 0.82], I = 41.5%), respectively.ConclusionNasal NO has the potential as a noninvasive diagnostic measure and endotype tool for ECRS.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".