How to manage asthma in a food-allergic patient: A practical approach
Bibliographic record
Abstract
This article examines the intricate interplay between food allergy and asthma by focusing on the challenges of managing food allergy in patients with asthma. Although asthma is a prevalent comorbidity in food allergy, often > 50% among older children, its impact on food allergy outcomes remains not fully understood. Studies indicate a higher prevalence of asthma among individuals experiencing fatal anaphylaxis, yet there is no consistent evidence that supports a direct causal link between food allergy and the development of asthma. Clinicians and patients often overestimate asthma control, which leads to underestimated risks associated with severe food allergy reactions. This article underscores the critical need for optimal asthma control before and during oral food challenges and dietary advancement therapies, highlighting the heightened risk of severe reactions in individuals with poorly controlled asthma. Whereas biologics such as omalizumab show promise in enhancing asthma control and increasing food allergy thresholds, a comprehensive, multifaceted approach that involves diligent asthma management, patient education, and appropriate treatment strategies is essential for ensuring safe and effective management of food allergy in individuals with asthma. Integrated management, which addresses both conditions concurrently, is vital for improving patient safety and quality of life.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".