Biomarkers associated with persistence and severity of IgE-mediated food allergies: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The prevalence of food allergies (FA) has increased worldwide over the last few decades. Milk, eggs, and peanuts are among the most common allergens and can cause anaphylaxis. Therefore, we aimed to identify biomarkers that could predict the persistence and/or severity of IgE-mediated allergies to milk, eggs, and peanuts via a systematic review. METHODS: This systematic review proceeded according to a protocol registered in the International Prospective Register of Systematic Reviews. Two independent authors extracted studies of interest from PubMed, SciELO, EMBASE, Scopus, and Ebsco databases and assessed their quality using the Newcastle-Ottawa Scale. RESULTS: We selected 14 articles describing 1,398 patients. Among eight identified biomarkers, total IgE, specific IgE (sIgE), and IgG4 were the most often cited biomarkers of persistent allergies to milk, eggs, and peanuts. Skin prick tests, endpoint tests, and sIgE cutoff levels may predict positive responses to challenges with these foods. The basophil activation test is a biomarker for the severity and/or threshold of allergic reactions to milk and peanuts. CONCLUSION: Only a few publications identified possible prognostic indicators of the persistence or severity of FA and outcomes of oral food challenges, indicating that more accessible biomarkers are needed to determine the likelihood of having a severe food allergic reaction.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".