Primary, secondary and tertiary prevention of food allergy: current practices and future directions
Bibliographic record
Abstract
The incidence of food allergies has risen around the globe, and experts have been exploring methods of preventing such allergies in young children to ease the burden of disease and reduce the morbidity and mortality caused by anaphylaxis to food allergens. Such preventative measures can be categorised as primary, secondary and tertiary prevention, which are discussed in detail in this review. Primary prevention is defined as the prevention of becoming sensitised towards specific allergens. The evidence suggests that avoiding common allergenic foods during pregnancy and breastfeeding is not protective against food allergies, and guidelines recommend weaning from 4 to 6 months of age, with recent studies supporting the early introduction of peanuts at 4 months to prevent peanut allergy. Secondary prevention targets patients who are already sensitised and aims to halt the progression of sensitisation, with evidence for high rates of success and safety in trials of early introduction to milk and peanuts using oral immunotherapy in sensitised infants. Tertiary allergy prevention focuses on reducing the risk of a patient having anaphylaxis, with oral immunotherapy being the most common method of promoting tolerance in allergic children. Several studies have demonstrated successful reintroduction for milk, egg and peanut; however, no such guidelines are recommended for other foods. Finally, dietary advancement therapy in the form of milk and egg ladders has been employed as a method of primary, secondary and tertiary prevention of allergies, particularly in Ireland, the UK and Canada.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".