Bypassing the build-up phase for oral immunotherapy in shrimp-allergic children
Bibliographic record
Abstract
Background: Oral immunotherapy is an effective treatment for food allergies; however, its use in clinical practice is limited by resources and lack of standardized protocols for foods other than peanut. Previous studies have suggested that shrimp has a higher threshold for reaction than other allergenic foods, suggesting it may be safe to directly administer maintenance doses of immunotherapy. Methods: Children aged 3-17 years who had 1) skin prick test ≥3 mm and/or specific IgE level ≥0.35 kU/L and convincing objective IgE-mediated reaction to shrimp, or 2) no ingestion history and specific IgE level ≥5 kU/L, underwent a low-dose oral food challenge to 300 mg shrimp protein, with the goal of continuing daily ingestion of the 300 mg maintenance dose as oral immunotherapy. Results: Between January 2020 and April 2023, 17 children completed the low-dose oral food challenge. Nine (53%) tolerated this amount with no reaction, and 8 (47%) had a mild reaction (isolated oral pruritis or redness on chin). Sixteen (94%) continued maintenance low-dose oral immunotherapy eating 300 mg shrimp protein daily. None of the patients developed anaphylaxis related to the immunotherapy. Conclusion: Our case series suggests that some shrimp allergic patients being considered for oral immunotherapy should be offered a low-dose oral food challenge, to potentially bypass the build-up phase of 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".