Intranasal Versus Oral Treatments for Allergic Rhinitis: A Systematic Review With Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Treatments for allergic rhinitis include intranasal or oral medications. OBJECTIVE: To perform a systematic review with meta-analysis comparing the effectiveness of intranasal corticosteroids or antihistamines versus oral antihistamines or leukotriene receptor antagonists in improving allergic rhinitis symptoms and quality of life. METHODS: We searched four bibliographic databases and three clinical trial datasets for randomized controlled trials (1) assessing patients aged 12 years and older with seasonal or perennial allergic rhinitis, and (2) comparing intranasal corticosteroids or antihistamines versus oral antihistamines or leukotriene receptor antagonists. We performed a meta-analysis of the Total Nasal Symptom Score (TNSS), Total Ocular Symptom Score, Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ), development of adverse events, and withdrawals owing to adverse events. Certainty of evidence was assessed using Grading of Recommendations, Assessment, Development, and Evaluation. RESULTS: = 0%). CONCLUSIONS: Randomized controlled trials suggest that intranasal treatments are more effective than oral treatments at improving symptoms and quality of life in seasonal allergic rhinitis.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".