Oral Immunotherapy for Food-Allergic Children: A Pro-Con Debate
Bibliographic record
Abstract
The prevalence of food allergy has increased in recent years, especially in children. Food allergen avoidance and symptomatic drugs in case of an allergic reaction remain the standard of care in food allergy. Nevertheless, increasing attention has been given to the possibility to treat food allergy, through immunotherapy, particularly oral immunotherapy (OIT). Several OIT protocols and clinical trials have been published. Most of them focus on children allergic to milk, egg, or peanuts, although recent studies developed protocols for other foods, such as wheat and different nuts. OIT efficacy in randomized controlled trials is usually evaluated as the possibility for patients to achieve desensitization, while the issue of a possible long-term sustained unresponsiveness has not been completely addressed. Here, we evaluated current OIT knowledge, focusing on the results of clinical trials and current guidelines. Specifically, we wanted to highlight what is known in terms of OIT efficacy and effectiveness, safety, and impact on quality of life. For each aspect, we reported the pros and the cons, inferable from published literature. In conclusion, even though many protocols, reviews and meta-analysis have been published on this topic, OIT remains a controversial therapy and no definitive generalized conclusion may be drawn so far. It should be an option provided by specialized teams, when both patients and their families are prone to adhere to the proposed protocol. Efficacy, long-term effectiveness, possible role of adjuvant therapies, risk of severe reactions including anaphylaxis or eosinophilic esophagitis, and impact on the quality of life of both children and caregivers are all aspects that should be discussed before starting OIT. Future studies are needed to provide firm clinical and scientific evidence, which should also consider patient reported outcomes.
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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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".