The acute and long‐term management of food allergy: protocol for a rapid systematic review
Bibliographic record
Abstract
BACKGROUND: Allergic reactions to plant and animal derived food allergens can have serious consequences for sufferers and their families. The associated social, emotional and financial costs make it a priority to understand the best ways of managing such immune-mediated hypersensitivity responses. Conceptually, there are two main approaches to managing food allergy: those targeting immediate symptoms and those aiming to support long-term management of the condition. The European Academy of Allergy and Clinical Immunology is developing guidelines about what constitutes an effective treatment for food allergies. As part of the guidelines development process, a systematic review is planned to examine published research about the management of food allergy in adults and children. METHODS: Seven bibliographic databases were searched from their inception to September 30, 2012 for systematic reviews, randomized controlled trials, quasi-randomized controlled trials, controlled clinical trials, controlled before-and-after studies and interrupted time series. Experts were consulted for additional studies. There were no language or geographic restrictions. Studies were critically appraised using the Critical Appraisal Skills Program and Cochrane EPOC Risk of Bias tools. Only studies where people had a diagnosis of food allergy or reported a history of food allergy were included. This means that many studies of conditions that may be caused by food allergy are omitted, because only research in people with an explicit diagnosis or history was eligible. DISCUSSION: Many initiatives have been tested to treat the immediate symptoms of food allergy (acute management) and to deal with longer lasting symptoms or induce tolerability to potential allergens (long-term management). The best management strategies for people with food allergy are likely to depend on the type of allergy, symptom manifestations and age. There is a real need to increase the amount of high quality research devoted to treatment strategies for food allergy. Food allergy can be debilitating and is affecting an increasing number of children and adults. With such little known about how to effectively manage the condition and its manifestations, this appears a priority for future research.
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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 | 0.076 | 0.096 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.023 | 0.018 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.094 | 0.013 |
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".