Rupatadine to prevent local allergic reactions to sublingual allergy immunotherapy: a case series
Bibliographic record
Abstract
BACKGROUND: Sublingual immunotherapy tablets (SLIT-T) are an effective treatment for allergic rhinitis (AR), but some patients experience local allergic reactions (LAR) in the first few weeks of treatment that can lead to treatment discontinuation. Although oral antihistamines are recommended for the treatment and pretreatment of LAR associated with SLIT-T, there are no clinical trial data to support this recommendation. Rupatadine is an H1 antihistamine that also inhibits platelet activating factor activity. The objective of this case series is to describe real-world clinical situations in which rupatadine was used to treat or mitigate SLIT-T-related LAR. CASE PRESENTATIONS: Five cases are presented by the managing allergist and off-label use of rupatadine is their expert opinion only. Patients in all 5 cases were treated with a SLIT-T (e.g. ragweed, tree, grass, or house dust mites) for the management of allergic rhinitis and experienced bothersome LAR with the first SLIT-T administration. In 3 cases, rupatadine 10 mg was administered for the immediate treatment of LAR (either in-office with the first SLIT-T dose or for subsequent LAR experienced at home) and the symptoms resolved. In 3 cases, pretreatment with other second-generation H1 antihistamines was unable to prevent LAR and the patients discontinued the SLIT-T. In these 3 cases, switching to pretreatment with rupatadine allowed the patients to restart and tolerate SLIT-T treatment with minimal or no LAR. In these patients with an established history of LAR, proactive pretreatment with rupatadine in subsequent seasons or with initiation of a different SLIT-T mitigated the previously experienced LARs. CONCLUSIONS: In the cases presented, treatment with rupatadine resolved LAR associated with SLIT-T treatment and rupatadine pretreatment appeared to mitigate subsequent LAR. Rupatadine may be an option to treat or improve the tolerability of the SLIT-T, potentially improving early treatment persistence.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".