Food Allergy
Bibliographic record
Abstract
Abstract Food allergy has recently become more common. There is no curative treatment for food allergy and management relies on allergen avoidance and emergency treatment of accidental allergic reactions. Food allergy can have a significant impact on the lives of patients and their families and an accurate diagnosis is, therefore, of utmost importance. A food allergy‐focused clinical history, together with evidence of allergen‐specific IgE, can be enough to confirm or exclude the diagnosis of food allergy; however, in the equivocal cases, exposure to the allergen in a controlled environment in hospital, called oral food challenge (OFC), is required. New tests are being developed to reduce the number of patients that need OFCs. Allergen‐specific immunotherapy has shown to reduce patients' sensitivity to the allergen but the majority of treated patients still remain food allergic. In the absence of a definitive treatment, prevention of food allergy is key. Key Concepts Food allergy prevalence is increasing, affecting about 7% of children and 2% of adults, with cow's milk, egg, peanut, tree nuts, sesame, fish and shellfish constituting more than 90% of food allergies in children. Risk factors for food allergy are atopic eczema, family history of atopic diseases, black or Asian ethnicity, less diverse diet, low serum vitamin D and filaggrin loss‐of‐function mutations. The diagnosis of food allergy is usually based on a combination of clinical history and evidence of allergen‐specific IgE, with oral food challenges being reserved for the equivocal cases. There is no curative treatment for food allergy; management consists of allergen avoidance and emergency treatment plan for accidental allergic reactions. Allergenic foods should be introduced in the diet at the time of weaning. Early peanut consumption in the first year of life can prevent the development of peanut allergy at school age.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.320 | 0.231 |
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".