Systematic review and meta-analyses on the accuracy of diagnostic tests for IgE-mediated food allergy
Bibliographic record
Abstract
Abstract: Background: The European Academy of Allergy and Clinical Immunology’s (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. Methods : We searched three databases (Cochrane CENTRAL (Trials), MEDLINE (OVID) and Embase (OVID)) for diagnostic test accuracy studies published between 1 st October 2012 and 30 th 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 combination where 3 or more studies were available. Results : 149 studies comprising 24,489 patients met the inclusion criteria and were generally heterogeneous. 60.4% of studies were in children ≤12 years of age, 54.3% undertaken in Europe, ≥95% conducted in a specialized pediatric or allergy clinical setting and all included oral food challenge in at least a percentage of enrolled patients, in 21.5% DBPCFC. 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 to individual components had high specificity: Ara h 2 had 92%, Cor a 14 95%, Ana o 3 94%, casein 93%, ovomucoid 92/91% for the diagnosis of peanut, hazelnut, cashew, cow’s milk and raw/cooked egg allergies, respectively. BAT was highly specific for the diagnosis of peanut (90%) and sesame (93%) allergies. Conclusions: 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. PROSPERO registration: CRD42021259186 Funding: European Academy of Allergy (EAACI).
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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 | 0.034 | 0.097 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.048 |
| 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) | 0.004 | 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".