Safety and Effectiveness of Bypassing Oral Immunotherapy Buildup With an Initial Phase of Sublingual Immunotherapy for Higher-Risk Food Allergy
Bibliographic record
Abstract
BACKGROUND: Because of its favorable safety, sublingual immunotherapy (SLIT) for food allergy has been proposed as an alternative treatment for those in whom oral immunotherapy (OIT) is of higher risk-older children, adolescents, adults, and those with a history of severe reactions. Although safe, SLIT has been shown to be less effective than OIT. OBJECTIVE: To describe the safety of multifood SLIT in pediatric patients aged 4 to 18 years and the effectiveness of bypassing OIT buildup with an initial phase of SLIT. METHODS: Patients aged 4 to 18 years were offered (multi)food SLIT. Patients built up to 2 mg protein SLIT maintenance over the course of 3 to 5 visits under nurse supervision. After 1 to 2 years of daily SLIT maintenance, patients were offered a low-dose oral food challenge (OFC) (cumulative dose, 300 mg protein) with the goal of bypassing OIT buildup. RESULTS: Between summer 2020 and winter 2023, 188 patients were enrolled in SLIT (median age, 11 years). Four patients (2.10%) received epinephrine during buildup and went to the emergency department, but none experienced grade 4 (severe) reaction. A subset of 20 patients had 50 low-dose OFCs to 300 mg protein and 35 (70%) OFCs were successful, thereby bypassing OIT buildup. CONCLUSIONS: In combination with very favorable safety of SLIT, with no life-threatening reactions and few reactions requiring epinephrine, we propose that an initial phase of SLIT to bypass supervised OIT buildup be considered for children in whom OIT is considered to be of higher risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".