Managing food allergy: GA2LEN guideline 2022
Bibliographic record
Abstract
LEN). A multidisciplinary international Task Force developed the guideline using the Appraisal of Guidelines for Research and Evaluation (AGREE) II framework and the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. We reviewed the latest available evidence as of April 2021 (161 studies) and created recommendations by balancing benefits, harms, feasibility, and patient and clinician experiences. We suggest that people diagnosed with food allergy avoid triggering allergens (low certainty evidence). We suggest that infants with cow's milk allergy who need a breastmilk alternative use either hypoallergenic extensively hydrolyzed cow's milk formula or an amino acid-based formula (moderate certainty). For selected children with peanut allergy, we recommend oral immunotherapy (high certainty), though epicutaneous immunotherapy might be considered depending on individual preferences and availability (moderate certainty). We suggest considering oral immunotherapy for children with persistent severe hen's egg or cow's milk allergy (moderate certainty). There are significant gaps in evidence about safety and effectiveness of the various strategies. Research is needed to determine the best approaches to education, how to predict the risk of severe reactions, whether immunotherapy is cost-effective and whether biological therapies are effective alone or combined with allergen immunotherapy.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".