Systematic review and meta‐analyses on the accuracy of diagnostic tests for IgE‐mediated food allergy
Bibliographic record
Abstract
The European Academy of Allergy and Clinical Immunology (EAACI) is updating the Guidelines on Food Allergy Diagnosis. We aimed to undertake a systematic review of the literature with meta-analyses to assess the accuracy of diagnostic tests for IgE-mediated food allergy. We searched three databases (Cochrane CENTRAL (Trials), MEDLINE (OVID) and Embase (OVID)) for diagnostic test accuracy studies published between 1 October 2012 and 30 June 2021 according to a previously published protocol (CRD42021259186). We independently screened abstracts, extracted data from full texts and assessed risk of bias with QUADRAS 2 tool in duplicate. Meta-analyses were undertaken for food-test combinations for which three or more studies were available. A total of 149 studies comprising 24,489 patients met the inclusion criteria and they were generally heterogeneous. 60.4% of studies were in children ≤12 years of age, 54.3% were undertaken in Europe, ≥95% were conducted in a specialized paediatric or allergy clinical setting and all included oral food challenge in at least a percentage of enrolled patients, in 21.5% double-blind placebo-controlled food challenges. Skin prick test (SPT) with fresh cow's milk and raw egg had high sensitivity (90% and 94%) for milk and cooked egg allergies. Specific IgE (sIgE) to individual components had high specificity: Ara h 2-sIgE had 92%, Cor a 14-sIgE 95%, Ana o 3-sIgE 94%, casein-sIgE 93%, ovomucoid-sIgE 92/91% for the diagnosis of peanut, hazelnut, cashew, cow's milk and raw/cooked egg allergies, respectively. The basophil activation test (BAT) was highly specific for the diagnosis of peanut (90%) and sesame (93%) allergies. In conclusion, SPT and specific IgE to extracts had high sensitivity whereas specific IgE to components and BAT had high specificity to support the diagnosis of individual food allergies.
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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 | 0.036 | 0.087 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.050 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".