Monitoring sputum eosinophils in mucosal inflammation and remodelling: a pilot study
Bibliographic record
Abstract
Normalisation of eosinophil counts in sputum of asthmatic patients reduces eosinophilic exacerbations. However, the effect of this strategy on airway remodelling remains to be determined. We compared bronchial inflammation and collagen deposition after 2 yrs of treatment guided by either sputum eosinophils (sputum strategy, SS) or by clinical criteria (clinical strategy, CS). As a pilot study, 20 mild asthmatic patients were randomly assigned to CS or SS strategies. Bronchial biopsies were obtained when minimum treatment needed to maintain control was identified and this was continued for 2 yrs. Biopsies were immunostained for inflammatory cells, mucin 5A (MUC5A) and collagen. The mean dose of inhaled corticosteroids at the start and end of the study was similar in both SS and CS groups. Forced expiratory volume in 1 s increased in both groups at the study end. In SS, mucosal lymphocyte and eosinophil counts, but not neutrophils, were reduced at the end of the study. In CS, only activated eosinophil and neutrophil counts decreased. MUC5A staining decreased in SS but not CS. No change in collagen deposition underneath the basement membrane was observed in either strategy. Treatment strategies that normalise sputum eosinophils also reduce mucosal inflammatory cells and MUC5A expression, but do not change subepithelial collagen deposition in mild to moderate asthma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".