Benefit–harm analysis of azithromycin for the prevention of acute exacerbations of chronic obstructive pulmonary disease
Bibliographic record
Abstract
BACKGROUND: Low-dose oral azithromycin therapy is recommended as a preventive treatment for acute exacerbations of COPD. However, the overall benefit-harm balance of this treatment has not been well studied. METHODS: A probabilistic Markov model of COPD was created to simulate the course of COPD over 20 years. The model was populated with evidence from the literature and dedicated data analysis. The benefit of azithromycin was modelled as a reduction in exacerbation rates. Adverse events, including cardiovascular events, hearing loss, gastrointestinal symptoms and antimicrobial resistance (leading to a gradual decline in the effectiveness of azithromycin), were considered. All outcomes were assigned a health-related utility weight to estimate the overall net change in the quality-adjusted life year (QALY) associated with the use of azithromycin. RESULTS: In patients with a positive exacerbation history, azithromycin resulted in a net QALY gain of 17.9 per 100 patients (99.8% probability of expected QALY gain) over 20 years. The net benefit increased to 21.8 QALYs per 100 patients (99.9% probability of expected QALY gain) among the 'frequent exacerbator' subgroup. Azithromycin was not net beneficial among those without any moderate/severe exacerbations in the previous year. Findings were robust against series of sensitivity, scenario and threshold analyses. CONCLUSIONS: Long-term therapy with azithromycin confers a net benefit to ex-smoker patients with COPD with a recent history of exacerbations and an even larger benefit to those who are frequent exacerbators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".