The efficacy and safety of long-term add-on treatment of azithromycin in asthma
Bibliographic record
Abstract
AIM: Effects of azithromycin on asthma reported in clinical trials are less consistent. We aimed to further clarify the efficacy and safety of azithromycin in treatment of asthma. METHODS: The protocol registration number was CRD42017074318 (http://www.crd.york.ac.uk/Prospero). We searched PubMed, EMBASE, Cochrane databases, China National Knowledge Internet (CNKI), and Wanfang databases for the randomized controlled trials (RCTs) with prolonged treatment of azithromycin for more than 3 weeks. Random-effects or fixed-effects model was applied to calculate risk ratio (RR) and mean difference (MD) for dichotomous and continuous data respectively. RESULTS: A total of eight studies were included for analysis. The pooled result of adjunctive azithromycin therapy in asthma showed a small, but statistically significant increase in forced expiratory volume in one second (FEV1) (MD = 0.06, 95% confidence interval [CI]: 0.01-0.12, P = .02), but no significant differences in exacerbation frequency (MD = -0.42, 95%CI: -1.13 to 0.30, P = .25) and peak expiratory flow (PEF) (MD = 0.20, 95% CI: -0.05 to 0.44, P = .12), fractional exhaled nitric oxide (FeNO) (MD = 4.12, 95% CI: -2.06 to 10.30, P = .19), asthma quality of life questionnaire (AQLQ) (MD: 0.05, 95% CI: -0.17 to 0.28, P = .65), asthma control questionnaire (ACQ) (MD: -0.03, 95% CI: -0.21 to 0.15, P = .75). The subgroup analysis revealed that azithromycin could decrease FeNO among Asian asthma (MD = 15.04, 95% CI: 6.18-23.90, P = .0009). CONCLUSIONS: Add-on therapy of azithromycin in asthma patients could improve the FEV1, but failed to improve asthma exacerbations, PEF, ACQ, AQLQ, and FeNO. Subgroup analysis indicated that azithromycin could improve FeNO in Asian group asthmatics.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".