Managing egg allergy: A systematic review of traditional allergen avoidance methods and emerging graded exposure strategies
Bibliographic record
Abstract
Egg allergy represents a significant and growing health concern, particularly among young children. Consequently, there has been a surge in the development of management strategies to address this issue. While oral immunotherapy presents a promising novel approach, its resource-intensive nature renders it impractical in many countries. This review aims to contrast the traditional method of strict avoidance with emerging, cost-effective alternatives for managing egg allergy at home, such as the gradual introduction via a ladder approach. Studies were identified through the search of medical databases and gray literature, with a focus on studies spanning from 2003 to 2023. Studies were independently screened and appraised by two independent reviewers. One hundred and thirty-four articles were identified. After removing duplicates and screening, 49 underwent full-text review, resulting in 28 included articles. These encompassed various study designs and originated from multiple countries, primarily the USA, Australia and Canada. The interventions mainly focused on managing IgE-mediated egg allergy through graded exposure to denatured/baked egg (n = 20), with an additional six studies exploring allergen avoidance and two studies investigating both management methods. A key observation from this review is the shift in management strategies towards incorporating methods such as graded exposure to denatured/baked egg alongside traditional allergen avoidance methods. Allergen avoidance remains the cornerstone of egg allergy management. However, there is a need for complementary approaches to optimise outcomes for individuals with egg allergy. Factors such as quality of life, including social inclusion and dietary diversity, as well as economic implications are crucial considerations.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".