Specific IgE Decision Point Cutoffs in Children with IgE-Mediated Wheat Allergy and a Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Wheat IgE-mediated food allergy in children is one of the most frequent food allergies in westernized countries, affecting between 0.4 and 1% of children. Although 95% predictive decision points have been determined for major allergens such as peanut, egg, and milk, the diagnostic performances of wheat-specific IgE (sIgE) and wheat component testing are not well established. OBJECTIVES: The aim of this study was to determine sIgE decision point cutoffs in children with IgE-mediated wheat allergy and provide a review of the literature. METHOD: A retrospective review of wheat oral food challenges was performed at the pediatric allergy unit of the University Hospitals of Geneva between 2004 and 2019. Performance characteristics for wheat and ω-5 gliadin sIgE were calculated and positive and negative OFC data were compared using the Mann-Whitney U test. RESULTS: A wheat sIgE cutoff of 2.88 kUA/L had a sensitivity of 95% (negative decision point), whereas a cutoff of 78.1 kUA/L had a specificity of 95% (positive decision point). When giving equal weight to sensitivity and specificity, the optimal cutoff point for wheat sIgE was 12 kUA/L, which gave a specificity of 70% and a sensitivity of 66.67%. CONCLUSIONS: These findings suggest a high positive decision point for wheat sIgE (78.1 kUA/L). This reinforces the importance of considering OFC in children with IgE-mediated wheat allergy to confirm diagnosis even in patients with relatively high wheat sIgE values, as there is a risk of falsely mislabeling these patients as allergic.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".