Allergen immunotherapy and eosinophilic esophagitis: friends or foes?
Bibliographic record
Abstract
PURPOSE OF REVIEW: The connection between eosinophilic esophagitis (EoE) and food and airborne allergens is complex. Exposure to allergens (mainly food) is often the trigger for EoE flares. The development of EoE has been described as a side effect of allergen immunotherapy, especially oral immunotherapy (OIT, with food allergens), while isolated cases of EoE have been reported during sublingual immunotherapy (SLIT, with extracts of aeroallergens). RECENT FINDINGS: EoE is currently recognized as a common side effect of OIT, while a solid correlation between SLIT and EoE is missing. Animal models have been developed to study the pathophysiological link between sensitization to aeroallergens and the induction of EoE and will probably provide an interpretation of why there are cases of EoE developed during SLIT. Recent findings in animal models suggest a genetic connection to EoE development after sensitization and re-exposure to airborne allergens. Subcutaneous allergen immunotherapy does not have a causative effect on EoE; on the contrary, a beneficial effect on EoE has been reported. Moreover, epicutaneous immunotherapy with a vector containing milk has also been used to treat children with milk-induced EoE. SUMMARY: Discovering the immune links between allergens and EoE will further guide the proper use of allergen immunotherapy and help define future strategies for the management of EoE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".