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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".