Prevention of food allergy: can we stop the rise of IgE mediated food allergies?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Food allergy has become more prevalent in recent decades. Without a curative treatment for food allergy, prevention is key. Can we intervene and halt the food allergy epidemic? RECENT FINDINGS: There are three main hypotheses to explain the rise in food allergy: the dual-allergen exposure hypothesis, the hygiene hypothesis and the vitamin D hypothesis. In a recent systematic review of randomized controlled trials, only introduction of allergenic foods, namely egg and peanut, in the diet at the time of weaning and avoidance of temporary supplementation with cow's milk formula in the first few days of life showed low to moderate evidence of a preventive effect. SUMMARY: For primary prevention, introduction of allergenic foods at the time of weaning and avoidance of temporary supplementation with cow's milk formula in the first few days of life has been recommended. Introduction of foods once allergy has been excluded may be beneficial for sensitized subjects (secondary prevention). Once food allergy has been established, it is important to minimise complications (tertiary prevention) through allergen avoidance, timely treatment of allergic reactions, control of atopic co-morbidities and dietetic and psychological support, as appropriate. Immunomodulatory treatments can potentially be disease-modifying and require further research.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".