Higher inflammatory eosinophil levels in eosinophilic bronchitis compared to mild allergic asthma
Bibliographic record
Abstract
Rationale: Patients with chronic cough often can have eosinophilic bronchitis (EB) with asthma or non-asthmatic eosinophilic bronchitis, despite similar eosinophil levels to patients with allergic asthma (AA) without chronic cough. We investigated if the frequency of eosinophil subtypes in blood and sputum is different in AA compared to EB. Methods: Patients with EB (chronic cough, FEV1≥70% predicted, sputum eosinophils>2%) and patients with mild AA (FEV1≥70% predicted, methacholine PC20≤16mg/mL and skin-prick positive) provided blood and sputum that were stained for eosinophils (CD45+CD16-CD15+) expressing resident (rEOS:CD62L+CD123-) and inflammatory (iEOS:CD62L-CD123+) and activation marker CD69 and expressed as % of total eosinophils. Results: Blood (250μl and 300μl) and sputum (3.8% and 3.2%) eosinophils in AA and EB, respectively, were similar. EB had a significantly higher frequency of iEOS and CD123+ eosinophils in blood and sputum, and higher levels of CD69+ eosinophils in sputum. Conclusion: A higher frequency of iEOS in both circulation and airways together with elevated levels of the activation marker CD69 in sputum eosinophils of EB compared to AA demonstrates a higher overall level of activated eosinophils. It is unclear whether eosinophils contribute to the pathophysiology of EB, but ongoing clinical trials with anti-IL-5 therapy will determine their relationship to cough.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".