Fluticasone furoate nasal spray consistently and significantly improves both the nasal and ocular symptoms of seasonal allergic rhinitis: a review of the clinical data
Bibliographic record
Abstract
BACKGROUND: Allergic rhinitis (AR) is a highly prevalent disorder, which often manifests as both nasal (congestion, sneezing, itching and rhinorrhoea) and ocular (redness, watery eyes, itching and burning) symptoms. Until recently, efficacy against the ocular symptoms of AR has been inconsistent for any single intranasal corticosteroid (INS). Fluticasone furoate is an enhanced-affinity glucocorticoid with potent anti-inflammatory activity. OBJECTIVE: To assess better the efficacy of an INS in the treatment of both the nasal and ocular symptoms of seasonal AR (SAR). METHODS: Data from all four trials of fluticasone furoate nasal spray (FFNS) in the treatment of SAR are reviewed and critically considered. RESULTS: FFNS consistently and significantly improved the nasal and ocular symptoms of SAR in patients sensitised to several different seasonal allergens (grass, ragweed and mountain cedar pollen) in all trials. An integrated analysis of the results also confirmed improvements in both nasal and ocular symptom scores in previously under-represented adolescent patients treated with FFNS. CONCLUSION: FFNS is the first INS to show consistent nasal and ocular efficacy across all SAR trials.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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 | 0.001 |
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".