Sputum Neutrophilia Can Mask Eosinophilic Bronchitis During Exacerbations
Bibliographic record
Abstract
BACKGROUND: Exacerbations of airway disease are eosinophilic, neutrophilic, both or neither. The primary objective of the present study was to identify whether the treatment of a neutrophilic bronchitis can unmask an associated eosinophilia. METHODS: A retrospective survey of 2160 consecutive sputum cell counts from 1343 patients with airway disease was conducted to identify patients with an isolated neutrophilic bronchitis, which was defined as a sputum total cell count of greater than or equal to 12 x 10(6) cells/g of sputum and a proportion of neutrophils of 80% or greater. The characteristics of the patients who subsequently demonstrated sputum eosinophilia (3% or greater) within eight weeks of resolving the neutrophilia were compared with the patients who subsequently did not have sputum eosinophilia. RESULTS: Two hundred thirty-seven patients had 273 neutrophilic exacerbations. The sputum was re-examined within eight weeks in 65 patients (27.4%), of whom 38 (58.5%) had resolution of the neutrophilic bronchitis after treatment with an antibiotic. Of these 38 patients, 13 (34%) showed eosinophilia. CONCLUSIONS: A neutrophilic exacerbation of airway disease was observed to mask sputum eosinophilia in one-third of patients who had sputum cell counts available before and after antibiotic therapy. Hence, the absence of sputum eosinophilia during an infective exacerbation should not be used as an indication to reduce the dose of corticosteroids. To optimize therapy, repeat sputum cell count measurements are recommended after antibiotic treatment before changing corticosteroid treatment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".