Update on rupatadine in the management of allergic disorders
Bibliographic record
Abstract
In a review of rupatadine published in 2008, the primary focus was on its role as an antihistamine, with a thorough evaluation of its pharmacology and interaction with histamine H1 -receptors. At the time, however, evidence was already emerging of a broader mechanism of action for rupatadine involving other mediators implicated in the inflammatory cascade. Over the past few years, the role of platelet-activating factor (PAF) as a potent mediator involved in the hypersensitivity-type allergic reaction has gained greater recognition. Rupatadine has dual affinity for histamine H1 -receptors and PAF receptors. In view of the Allergic Rhinitis and its Impact on Asthma group's call for oral antihistamines to exhibit additive anti-allergic/anti-inflammatory properties, further exploration of rupatadine's anti-PAF effects was a logical step forward. New studies have demonstrated that rupatadine inhibits PAF effects in nasal airways and produces a greater reduction in nasal symptoms than levocetirizine. A meta-analysis involving more than 2500 patients has consolidated the clinical evidence for rupatadine in allergic rhinoconjunctivitis in adults and children (level of evidence Ia, recommendation A). Other recent advances include observational studies of rupatadine in everyday clinical practice situations and approval of a new formulation (1 mg/ml oral solution) for use in children. In this reappraisal, we revisit some key properties and pivotal clinical studies of rupatadine and examine new clinical data in more detail including studies that measured health-related quality of life and studies that investigated the efficacy and safety of rupatadine in other indications such as acquired cold urticaria, mosquito bite allergy and mastocytosis.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".