Factors affecting exhaled nitric oxide measurements: the effect of sex
Bibliographic record
Abstract
BACKGROUND: Exhaled nitric oxide (F(E)NO) measurements are used as a surrogate marker for eosinophilic airway inflammation. However, many constitutional and environmental factors affect F(E)NO, making it difficult to devise reference values. Our aim was to evaluate the relative importance of factors affecting F(E)NO in a well characterised adult population. METHODS: Data were obtained from 895 members of the Dunedin Multidisciplinary Health and Development Study at age 32. The effects of sex, height, weight, lung function indices, smoking, atopy, asthma and rhinitis on F(E)NO were explored by unadjusted and adjusted linear regression analyses. RESULTS: The effect of sex on F(E)NO was both statistically and clinically significant, with F(E)NO levels approximately 25% less in females. Overall, current smoking reduced F(E)NO up to 50%, but this effect occurred predominantly in those who smoked on the day of the F(E)NO measurement. Atopy increased F(E)NO by 60%. The sex-related differences in F(E)NO remained significant (p < 0.001) after controlling for all other significant factors affecting F(E)NO. CONCLUSION: Even after adjustment, F(E)NO values are significantly different in males and females. The derivation of reference values and the interpretation of FENO in the clinical setting should be stratified by sex. Other common factors such as current smoking and atopy also require to be taken into account.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".