Prophylactic azithromycin in preventing recurrent acute exacerbations of COPD and blood eosinophil counts: a hospital review
Bibliographic record
Abstract
Rationale: The CTS guidelines recommend considering prophylactic azithromycin for patients with recurrent acute exacerbations of chronic obstructive pulmonary disease (AECOPD) despite appropriate inhaled therapy. This study compared readmissions up to 6 months after hospital discharge in patients prescribed azithromycin, based on blood eosinophil counts (BEC). Methods: Subjects hospitalized between January 2022 and September 2024 for AECOPD at Montfort Hospital were reviewed. Patient characteristics recorded included age, sex, and cigarette smoking history. Respiratory medications were assessed, in addition to spirometry results and BEC from the past year. Readmissions for AECOPD were documented. Individuals prescribed azithromycin were compared based on peak BEC</≥0.3x109/L. Results: Among 481 patients treated for AECOPD, 50 (10%) were prescribed prophylactic azithromycin. 46 (92%) were on inhaled LAMA/LABA/ICS; 4 (8%) LAMA/LABA. 25 subjects (50%) had a peak BEC ≥0.3x109/L. Clinical characteristics of patients with peak BEC</≥0.3x109/L were similar. Notably, the number of individuals readmitted for AECOPD within 6 months was higher in the BEC ≥0.3x109/L group, 14 (58%) vs 7 (28%) (p=0.03). The mean number of AECOPD was also greater in such patients, 0.75 vs 0.24 (p=0.01). erj;66/suppl_69/PA2451/F1 F1 F1 Conclusion: Prophylactic azithromycin was more effective at reducing recurrent AECOPD in patients with lower BEC.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".