Efficacy and safety of intranasal medications for allergic rhinitis: Network meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Intranasal antihistamines (INAH), corticosteroids (INCS), and their fixed combinations (INAH+INCS) are one of the cornerstones of the treatment of allergic rhinitis (AR). We performed a systematic review and network-meta-analysis comparing the efficacy and safety of INAH, INCS, and INAH+INCS in patients with AR. METHODS: We searched four electronic bibliographic databases and three clinical trial databases for randomised controlled trials assessing the use of INAH, INCS, and INAH+INCS in adults with seasonal or perennial AR. We performed a network meta-analysis on the Total Nasal Symptom Score, Total Ocular Symptom Score, Rhinoconjunctivitis Quality-of-Life Questionnaire, development of adverse events, and withdrawals due to adverse events. Certainty of evidence was assessed using GRADE-NMA. RESULTS: We included 167 primary studies, most of which assessed patients with seasonal AR. Among individual medications, azelastine-fluticasone, and fluticasone furoate were the most frequently highest-ranked interventions for efficacy outcomes, being regularly associated with clinically meaningful larger improvements when compared to other active treatments. Considering drug classes, INAH+INCS were the highest-ranked interventions for all outcomes in which they were assessed, followed in most cases by INCS. In 105 out of 184 comparisons in seasonal AR, and 28 out of 97 comparisons in perennial AR, certainty of evidence was considered "high" or "moderate". CONCLUSION: Intranasal medications for AR display clinically relevant differences in their efficacy, but all show a good safety profile. To our knowledge, this is the first network meta-analysis comparing INAH, INCS, and INAH+INCS in AR, providing relevant evidence for guideline developers and practising physicians on the most efficacious treatments.
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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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.049 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".