Long-Term Safety of Epicutaneous Immunotherapy in Peanut-Allergic Children: An Open-Label Active Treatment (REALISE Study)
Bibliographic record
Abstract
BACKGROUND: Owing to limited treatment options for peanut allergy, patients remain at risk for allergic reactions due to accidental exposure. Epicutaneous immunotherapy (EPIT) is a novel treatment being investigated for peanut allergy. OBJECTIVE: This study assessed long-term safety of EPIT with VIASKIN peanut patch 250 μg (VP250) via an open-label extension of the REAL Life Use and Safety of EPIT (REALISE) trial. METHODS: REALISE was a phase 3 trial in peanut-allergic children aged 4 through 11 years that included a 6-month, randomized, double-blind, placebo-controlled treatment phase, followed by an open-label, single-arm, active treatment period for up to 36 months. RESULTS: Of the 392 participants (male 54.8%; median age 7.2 y) who received at least 1 dose of treatment, 77.8% completed the 36-month active treatment. Mean adherence to treatment was high at 96.4%. Most participants (98.7%) experienced at least 1 treatment-emergent adverse event (TEAE); the majority were mild or moderate and decreased in frequency and severity over time. Most participants (94.6%) experienced at least 1 treatment-related TEAE. Local skin reactions were the most common treatment-related TEAE with the incidence decreasing from year 1 (87.8%) to year 3 (19.2%). Serious treatment-related TEAEs were reported in 2 participants. No specific safety signals were identified in the 14 participants enrolled with a history of severe anaphylaxis (Anaphylaxis Staging System grade 3). CONCLUSION: Consistent with previous phase 3 studies, long-term EPIT with VIASKIN peanut patch 250 μg was well tolerated with high adherence in peanut-allergic children aged 4 through 11 years (clinicaltrials.gov; NCT: NCT02916446).
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".