Management of allergic rhinitis with leukotriene receptor antagonists versus selective H1-antihistamines: a meta-analysis of current evidence
Bibliographic record
Abstract
Abstract Background Inconsistencies remain regarding the effectiveness and safety of leukotriene receptor antagonists (LTRAs) and selective H1-antihistamines (SAHs) for allergic rhinitis (AR). A meta-analysis of randomized controlled trials (RCTs) was conducted to compare the medications. Methods Relevant head-to-head comparative RCTs were retrieved by searching the PubMed, Embase, and Cochrane’s Library databases from inception to April 20, 2020. A random-effects model was applied to pool the results. Subgroup analyses were performed for seasonal and perennial AR. Results Fourteen RCTs comprising 4458 patients were included. LTRAs were inferior to SAHs in terms of the daytime nasal symptoms score (mean difference [MD]: 0.05, 95% confidence interval [CI] 0.02 to 0.08, p = 0.003, I2 = 89%) and daytime eye symptoms score (MD: 0.05, 95% CI 0.01 to 0.08, p = 0.009, I2 = 89%), but were superior in terms of the nighttime symptoms score (MD: − 0.04, 95% CI − 0.06 to − 0.02, p < 0.001, I2 = 85%). The effects of the two treatments on the composite symptom score (MD: 0.02, 95% CI − 0.02 to 0.05, p = 0.30, I2 = 91%) and rhinoconjunctivitis quality-of-life questionnaire (RQLQ) (MD: 0.01, 95% CI − 0.05 to 0.07, p = 0.71, I2 = 99%) were similar. Incidences of adverse events were comparable (odds ratio [OR]: 0.97, 95% CI 0.75 to 1.25, p = 0.98, I2 = 0%). These results were mainly obtained from studies on seasonal AR. No significant publication bias was detected. Conclusions Although both treatments are safe and effective in improving the quality of life (QoL) in AR patients, LTRAs are more effective in improving nighttime symptoms but less effective in improving daytime nasal symptoms compared to SAHs.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.054 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".