Diagnosing IgE-mediated food allergy: How to apply the latest guidelines in clinical practice
Bibliographic record
Abstract
Background: People suspected of food allergy require accurate diagnosis to help manage their condition and get appropriate care. Recent guidelines summarize the latest evidence about diagnosing IgE-mediated food allergy, but they do not describe how to address practical issues when testing. There is a need to translate guideline recommendations into a practical common pathway that all centers dealing with food allergy can use. Objective: The Global Network of Centres of Excellence for Anaphylaxis & Food Allergy-ANAcare developed a pathway to help clinicians apply the latest diagnostic guidelines and overcome implementation challenges. Methods: The pathway is based on reviewing guidelines, research and clinical feedback, plus consensus of experts from 13 countries. Results: We describe practical tips that clinicians can use when taking a detailed clinical history and testing people with suspected IgE-mediated food allergy. Tests for IgE sensitization such as skin prick tests and specific IgE are readily available and inexpensive. However, they only demonstrate sensitization, not clinical allergic disease, so they need to be interpreted in the context of the clinical history. A controlled oral food challenge may also be needed to identify which foods the person is experiencing reactions to and what quantity can be tolerated. Conclusions: Correct diagnosis is essential to support individualized management. Allergy centers around the world can use our practical tips to help avoid under- and overdiagnosis.
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 | 0.041 | 0.161 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.015 |
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".