Sputum cell counts to manage prednisone-dependent asthma: effects on FEV1 and eosinophilic exacerbations
Bibliographic record
Abstract
BACKGROUND: Prednisone dependence in asthma is usually described based on clinical and spirometric characteristics. It is generally believed that these patients have frequent exacerbations and lose lung function rapidly because of uncontrolled airway eosinophilia. OBJECTIVES: The objectives of this study are to report the effect on asthma exacerbations and the change in lung function over time in prednisone-dependent asthma when severe asthma is managed using a protocol that aims to maintain normal sputum cell counts. METHODS: A retrospective survey of patients prospectively assessed in a university tertiary care asthma clinic. RESULTS: 52 patients (30 males, mean age 51 years, 64% non-atopic) were followed for a median period of 5.4 years (min-max: 0.2-35.2). Monitoring with the aim of keeping sputum eosinophils below 3% resulted in higher doses of corticosteroids (median daily dose of prednisone was 10 mg and for inhaled corticosteroids was 1500 μg of fluticasone equivalent) than at baseline and this was associated with predictable adverse effects. Despite the disease severity, 10 patients (19%) did not require LABA for symptom control. Most importantly, over the period of follow-up, there were only 0.3 eosinophilic exacerbations/patient/year. Overall, there was an increase in FEV1 over the period of follow-up (mean +84.6 ml/year) rather than an expected decline. CONCLUSIONS: Monitoring of eosinophils in sputum enables to maintain symptom control and preserve FEV1 in patients with severe prednisone-dependent 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".