Inhaled corticosteroids in COPD: the clinical evidence
Bibliographic record
Abstract
In this article, we focus on the scientific evidence from randomised trials supporting treatment with inhaled corticosteroids (ICS) in chronic obstructive pulmonary disease (COPD), including treatment with combinations of long-acting β-agonist (LABA) bronchodilators and ICS. Our emphasis is on the methodological strengths and limitations that guide the conclusions that may be drawn. The evidence of benefit of ICS and, therefore, of the LABA/ICS combinations in COPD is limited by major methodological problems. From the data reviewed herein, we conclude that there is no survival benefit independent of the effect of long-acting bronchodilation and no effect on FEV1 decline, and that the possible benefit on reducing severe exacerbations is unclear. Our interpretation of the data is that there are substantial adverse effects from the use of ICS in patients with COPD, most notably severe pneumonia resulting in excess deaths. Currently, the most reliable predictor of response to ICS in COPD is the presence of eosinophilic inflammation in the sputum. There is an urgent need for better markers of benefit and risk that can be tested in randomised trials for use in routine specialist practice. Given the overall safety and effectiveness of long-acting bronchodilators in subjects without an asthma component to their COPD, we believe use of such agents without an associated ICS should be favoured.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".