<scp>EAACI</scp> guidelines on the diagnosis of <scp>IgE</scp>‐mediated food allergy
Bibliographic record
Abstract
This European Academy of Allergy and Clinical Immunology guideline provides recommendations for diagnosing IgE-mediated food allergy and was developed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. Food allergy diagnosis starts with an allergy-focused clinical history followed by tests to determine IgE sensitization, such as serum allergen-specific IgE (sIgE) and skin prick test (SPT), and the basophil activation test (BAT), if available. Evidence for IgE sensitization should be sought for any suspected foods. The diagnosis of allergy to some foods, such as peanut and cashew nut, is well supported by SPT and serum sIgE, whereas there are less data and the performance of these tests is poorer for other foods, such as wheat and soya. The measurement of sIgE to allergen components such as Ara h 2 from peanut, Cor a 14 from hazelnut and Ana o 3 from cashew can be useful to further support the diagnosis, especially in pollen-sensitized individuals. BAT to peanut and sesame can be used additionally. The reference standard for food allergy diagnosis is the oral food challenge (OFC). OFC should be performed in equivocal cases. For practical reasons, open challenges are suitable in most cases. Reassessment of food allergic children with allergy tests and/or OFCs periodically over time will enable reintroduction of food into the diet in the case of spontaneous acquisition of oral tolerance.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.021 |
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".