An algorithm for the diagnosis and management of IgE‐mediated food allergy, 2024 update
Bibliographic record
Abstract
An algorithm for the diagnosis and management of IgE-mediated food allergy, 2024 update The European Academy of Allergy and Clinical Immunology (EAACI) recently launched their updated Clinical Guidelines for the Diagnosis and Management of IgE-mediated Food Allergy, which provide evidence-based recommendations for the practicing clinician seeing children and/or adults with suspected IgE-mediated food allergy. 1This medical algorithm aims to summarize the practical approach to individual patients considering both diagnosis (Figure 1) and management (Figure 2), following EAACI recommendations.The first and most valuable step to reaching an accurate diagnosis is a well-conducted detailed allergy-focused clinical history, including dietary history.Key questions are listed in the Clinical Guidelines. 1 It is important to ascertain, for each main allergenic food (e.g., cow's milk, egg, wheat, soya, fish, shellfish, peanut, tree nuts, sesame, legumes, fruits, and vegetables), whether these foods are consumed and whether there have been any possible allergic reactions.For foods that the patient is consuming regularly K E Y WO R DS adrenaline, basophil activation test, food allergy, diagnosis, IgE, immunotherapy, management, skin prick test, transcriptomics, treatment
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.018 |
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".