Efficacy and safety of allergen immunotherapy for IgE-mediated food allergy: systematic review and meta-analysis
Bibliographic record
Abstract
Background There is substantial interest in allergen-specific immunotherapy in food allergy. We systematically reviewed its efficacy and safety. Methods We searched six bibliographic databases from 1946 to 30 April 2021 for randomised controlled trials about immunotherapy alone or with biologicals in IgE-mediated food allergy confirmed by oral food challenge. We pooled the data using random-effects meta-analysis. Results We included 36 trials with 2,126 participants, mainly children. Oral immunotherapy increased tolerance whilst on therapy for peanut (RR 9.9, 95% CI 4.5. to 21.4, high certainty); cow’s milk (RR 5.7, 1.9 to 16.7, moderate certainty) and hen’s egg allergy (RR 8.9, 4.4 to 18, moderate certainty). The number needed to treat to increase tolerance to a single dose of 300mg or 1000mg peanut protein was 2. In peanut allergy, oral immunotherapy did not increase adverse reactions (RR 1.1, 1.0 to 1.2, low certainty) or severe reactions (RR 1,6, 0.7 to 3.5, low certainty). It may increase adverse reactions in cow’s milk (RR 3.9, 2.1 to 7.5, low certainty) and hen’s egg allergy (RR 7.0, 2.4 to 19.8, moderate certainty), but reactions tended to be mild and gastrointestinal. Epicutaneous immunotherapy increased tolerance whilst on therapy for peanut (RR 2.6, 1.8 to 3.8, moderate certainty). Results were unclear for other allergies and administration routes. Conclusions Oral immunotherapy improves tolerance whilst on therapy and is probably safe in peanut, cow’s milk and hen’s egg allergy. However, our review found little about whether this improves quality of life, is sustained or cost-effective.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".