The Use of Omalizumab in Food Oral Immunotherapy
Bibliographic record
Abstract
Food allergy is an important health issue that affects up to 8 % of the population. The management of allergic patients involves allergen avoidance and prompts the treatment of accidental reactions, as no curative treatment is available so far in routine practice. Oral immunotherapy (OIT) is a promising therapeutic alternative, but it is associated with frequent allergic reactions and cost-effectiveness issues. In hopes of reducing such reactions, a number of trials have used omalizumab, an anti-IgE monoclonal humanized antibody, as adjunctive therapy in OIT. The allergens studied in these omalizumab-enabled OIT trials include peanuts, milk, eggs, or mixes of multiple foods. In this article, we review the major findings from these studies and discuss potential benefits and issues related to omalizumab-enabled OIT. Results from the previous trials suggest that the use of omalizumab could potentially lead to safer and more efficient OIT protocols, by reducing the number and severity of reactions, and increasing allergen tolerance threshold. While more evidence is needed with regard to the maintenance of the long-term tolerance after OIT, omalizumab's potential immunomodulatory role could be of benefit. More studies are needed to further document this new indication for omalizumab.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".