Successful sublingual immunotherapy for severe egg allergy in children: a case report
Bibliographic record
Abstract
BACKGROUND: Egg allergy is one of the most common food allergies in children. To date, oral immunotherapy (OIT) has been considered as a promising treatment option for egg allergy. However, safety issues remain concerning severe adverse events requiring epinephrine injection. Hence, establishing a safer method to treat egg allergy would be beneficial. We report here two children with egg allergy who were safely treated with sublingual immunotherapy (SLIT) before transitioning to OIT. CASE PRESENTATION: Patient 1 was a 7-year-old girl and Patient 2 was a 5-year-old girl. Although OIT for egg had been attempted in both patients, severe anaphylactic symptoms were induced by ingesting only 0.1 g of heated whole egg. Therefore, SLIT was conducted with aqueous suspensions consisting of water and heated whole egg powder. Suspensions were administered sublingually, kept in the mouth for 2 min, and spat out immediately thereafter. SLIT was continued for 7 months for Patient 1 and 8 months for Patient 2 due to the exploratory character of the study. Afterwards, the patients successfully transferred to low-dose OIT with 1 g of heated whole egg (≒170 mg of egg protein) daily, and are continuing the therapy as of June 2020. As for adverse reactions, Patient 1 expressed oral cavity itchiness once at the beginning of SLIT. Patient 2 had no adverse reaction. The levels of antigen-specific IgE decreased in both patients after SLIT, and further decreased after switching to OIT. CONCLUSIONS: Few clinical studies have evaluated the efficacy and safety of SLIT for egg allergy. Although the treatment was conducted in only two patients, our results have shown that SLIT is a promising treatment procedure for egg allergy. Further clinical trials will be needed to additionally assess the efficacy and safety of SLIT in children with food allergy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".