Does unified allergic airway disease impact on lung function and type 2 biomarkers?
Bibliographic record
Abstract
Abstract The concept of the unified allergic airway disease (UAD) recognises the association between allergic inflammation in the upper and lower airways. Patients with asthma and concomitant allergic rhinitis experience more asthma-related primary and secondary care visits. We therefore aimed to determine differences in asthma control (asthma control questionnaire ACQ-6), lung function (spirometry) and T2 biomarkers (FeNO and Eos) in relation to the presence of allergic rhinitis in patients with allergic asthma. Retrospectively, we evaluated a cohort of 60 consecutive patients with persistent asthma attending our research unit for screening into clinical trials. All included subjects were receiving inhaled corticosteroids (ICS) and had a positive skin prick test (SPT) to at least one common aeroallergen to fulfil the criterion of allergic asthma. Patients with UAD had a diagnosis of allergic asthma in addition to established concomitant allergic rhinitis. T2 biomarkers were significantly higher in patients with allergic rhinitis in contrast to those without. FEV1 % predicted and FEF25-75 % predicted were also significantly lower in patients with concomitant allergic rhinitis. However, there was no difference in ACQ-6 observed between groups. In summary, patients with allergic asthma, the presence of concomitant allergic rhinitis is associated with worse lung function and higher type 2 biomarkers.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".