The complex pathophysiology of allergic rhinitis: scientific rationale for the development of an alternative treatment option
Bibliographic record
Abstract
Allergic rhinitis (AR) poses a global health problem and can be challenging to treat. Many of the current symptomatic treatments for AR have been available for decades, yet there has been little improvement in patient quality of life or symptom burden over the years. In this review, we ask why this might be and explore the pathophysiological gaps that exist within the various AR treatment classes. We focus on the benefits and drawbacks of different treatment options and delivery routes for AR treatments and consider how, given what is known about AR pathophysiology and symptomatology, patients may be offered more effective treatment options for rapid, effective, and sustained AR control. In particular, we consider how a new AR preparation, MP-AzeFlu (Dymista ® , Meda, Sweden), comprising a formulation of an intranasal antihistamine (azelastine hydrochloride), an intranasal corticosteroid (fluticasone propionate), and excipients delivered in a single spray, may offer benefits over and above single and multiple AR therapy options. We review the evidence in support of this treatment across the spectrum of AR disease. The concept of AR control is also reviewed within the context of new European Union and Contre les Maladies Chroniques pour un VIeillissement Actif-Allergic Rhinitis and its Impact on Asthma initiatives.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".