Protocol for a systematic review of the diagnostic test accuracy of tests for IgE-mediated food allergy
Bibliographic record
Abstract
Background: The European Academy of Allergy and Clinical Immunology (EAACI) is in the process of updating the guidelines on the diagnosis and management of food allergy. The existing guidelines are based on a systematic review of the literature until 30th September 2012. Therefore, a new systematic review must be undertaken to inform the new guidelines. This systematic review aims to assess the accuracy of index tests to support the diagnosis of IgE-mediated food allergy. Methods: The databases Cochrane CENTRAL (Trials), MEDLINE (OVID) and Embase (OVID) will be searched for diagnostic test accuracy studies from 1st October 2012 to 30th June 2021. Inclusion and exclusion criteria will be used to select appropriate studies. Data from these studies will be extracted and tabulated, and then reviewed for risk of bias and applicability using the QUADAS-2 tool. All evaluation will be done in duplicate. Studies with a high risk of bias and low applicability will be excluded. Meta-analysis will be performed if there are three or more studies of the same index test and food. Results: A protocol for the systematic review and meta-analyses is presented and was registered using Prospero prior to commencing the literature search. Discussion: Oral food challenges are the reference standard for diagnosis but involve considerable risks and resources. This protocol for systematic review aims to assess the accuracy of various tests to diagnose food allergy, which can be useful in both clinical and research settings.
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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 | 0.091 | 0.168 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.019 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.117 | 0.017 |
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".