Safety and feasibility of oral immunotherapy to multiple allergens for food allergy
Bibliographic record
Abstract
BACKGROUND: Thirty percent of children with food allergy are allergic to more than one food. Previous studies on oral immunotherapy (OIT) for food allergy have focused on the administration of a single allergen at the time. This study aimed at evaluating the safety of a modified OIT protocol using multiple foods at one time. METHODS: Participants underwent double-blind placebo-controlled food challenges (DBPCFC) up to a cumulative dose of 182 mg of food protein to peanut followed by other nuts, sesame, dairy or egg. Those meeting inclusion criteria for peanut only were started on single-allergen OIT while those with additional allergies had up to 5 foods included in their OIT mix. Reactions during dose escalations and home dosing were recorded in a symptom diary. RESULTS: Forty participants met inclusion criteria on peanut DBPCFC. Of these, 15 were mono-allergic to peanut and 25 had additional food allergies. Rates of reaction per dose did not differ significantly between the two groups (median of 3.3% and 3.7% in multi and single OIT group, respectively; p = .31). In both groups, most reactions were mild but two severe reactions requiring epinephrine occurred in each group. Dose escalations progressed similarly in both groups although, per protocol design, those on multiple food took longer to reach equivalent doses per food (median +4 mo.; p < .0001). CONCLUSIONS: Preliminary data show oral immunotherapy using multiple food allergens simultaneously to be feasible and relatively safe when performed in a hospital setting with trained personnel. Additional, larger, randomized studies are required to continue to test safety and efficacy of multi-OIT. TRIAL REGISTRATION: Clinicaltrial.gov NCT01490177.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".