Bibliographic record
Abstract
Abstract Background: Bronchiectasis is a heterogenous condition characterized by cough, sputum production and recurrent infections. It is generally thought that the main driver of symptoms, exacerbations and disease progression is infection or neutrophilic inflammation. In recent years, there has been a focus on understanding the underlying inflammation and infection driving disease and how best to target therapy. Sputum cell count and differential (DCC) characterizes the type of airway inflammation to help tailor treatment to a specific patient. Eosinophilic inflammation predicts a response to corticosteroids, while neutrophilic inflammation may indicate infection and to consider antibiotic therapy. There are recent studies assessing peripheral eosinophilia in bronchiectasis, however there is limited data regarding sputum DCC to characterize airway inflammation in non-CF bronchiectasis. Our study aims to estimate the prevalence of various types of airway inflammation in bronchiectasis at our center. Additionally, we seek to determine if airway inflammation patterns differ among distinct aetiologies of non-CF bronchiectasis, evaluate whether peripheral eosinophilia serves as a predictor of airway eosinophilia in this population, and assess the prevalence of corticosteroid use in bronchiectasis management. Methods: This is a single centre retrospective study using a pre-existing sputum database. We analyzed collected sputum DCC results of patients with the referral reason of “bronchiectasis”. We used subtypes of bronchitis to characterize the inflammation. Patient characteristics were extracted via electronic chart review. We reviewed patient demographics, etiology of bronchiectasis, comorbidities, smoking history, medications, blood eosinophils, sputum colour, and clinical status at time of collection, if stable or during exacerbation. Results: Samples were collected from 2004-2024. 497 samples had a referral reason of “bronchiectasis”, 39 were excluded as they had cystic fibrosis, fibrotic interstitial lung disease, or did not have confirmed bronchiectasis on imaging. Preliminary results indicate that in this cohort, 8.7% of samples exhibited eosinophilic inflammation, 56.8% were neutrophilic, 15.1% showed mixed eosinophilic and neutrophilic inflammation, and 19.4% were classified as normal with no evidence of inflammation. Further analysis will explore correlations between airway inflammation subtypes and the underlying etiology of bronchiectasis, peripheral eosinophilia, and ongoing therapies, particularly the use of inhaled corticosteroids. Conclusions: A diverse range of airway inflammation patterns is observed in patients with non-CF bronchiectasis. Most patients showed neutrophilic inflammation, while 23.8% had evidence of eosinophilic inflammation, either alone or combined with neutrophilic inflammation. Sputum DCC may be a valuable tool for characterizing airway inflammation in non-CF bronchiectasis patients and has the potential to inform targeted therapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".