EAACI guidelines on the management of IgE‐mediated food allergy
Bibliographic record
Abstract
This European Academy of Allergy and Clinical Immunology (EAACI) guideline provides recommendations for the management of IgE-mediated food allergy and was developed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. Following the confirmation of IgE-mediated food allergy diagnosis, allergen avoidance and dietary advice (with support of a specialised dietitian, if possible) together with the provision of a written treatment plan, education on the recognition of allergic symptoms and prescription of medication including adrenaline using an auto-injector are essential. Patients with significant anxiety and requirement for coping strategies may benefit from support from a clinical psychologist. As immunomodulatory interventions, omalizumab is suggested for treatment of IgE-mediated food allergy in children from the age of 1 and adults; and oral allergen-specific immunotherapy is recommended for children and adolescents with peanut allergy and suggested for milk and egg allergies (generally after 4 years of age for milk and egg). Sublingual and epicutaneous immunotherapy are suggested for peanut allergy but are not yet available at the point of care. Future research into disease modifying treatments for IgE-mediated food allergy are highly needed, with standardised and patient-focused protocols and outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| 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 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".