Fractional Exhaled Nitric Oxide as an Inflammatory Biomarker in Chronic Obstructive Pulmonary Disease (COPD) with or without Concurrent Diagnosis of Asthma: The Canadian Cohort Obstructive Lung Disease (CanCOLD)
Bibliographic record
Abstract
We studied whether fractional exhaled nitric oxide (FENO) can differentiate chronic obstructive pulmonary disease (COPD) with concurrent diagnosis of asthma from COPD-only as well as its ability to predict disease severity and progression.This study was embedded in the Canadian Cohort Obstructive Lung Disease (CanCOLD). Subjects of ≥40 years old completed FENO measurements were subdivided into four groups, including COPD (N = 86 [COPD-only (N = 35) and COPD with concurrent diagnosis of asthma (N = 51)], healthy (N = 72), and at risk (N = 151). Three of the most common clinical definitions were used for characterizing COPD with concurrent diagnosis of asthma: 1) atopy and self-reported physician diagnosis of asthma, 2) ≥12% and ≥200 ml post-bronchodilator FEV1; 3) self-reported physician diagnosis of asthma. FENO values were classified using quartiles and the American Thoracic Society (ATS) guideline 2011.Compared to COPD-only, more COPD with concurrent diagnosis of asthma had a significant FENO50 level of ≥ 33.5 ppb (fourth quartile) than COPD-only (p = 0.045, 0.011, and 0.006, for definition 1, 2, and 3, respectively). Considering the ATS guideline 2011, fewer COPD with concurrent diagnosis of asthma had FENO50 < 25 than COPD-only, which was statistically significant with definition 1 and 3 (p = 0.038 and 0.026, respectively).FENO as a biomarker has the potential to be used as a complementary value for differentiating COPD with concurrent diagnosis of asthma from COPD-only. Further studies should be conducted on validated definitions of COPD with concurrent diagnosis of asthma, which may include a reference to the type of airway inflammation in addition to the clinical definition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".