Home-Based Peanut Oral Immunotherapy for Low-Risk Peanut-Allergic Preschoolers During the COVID-19 Pandemic and Beyond
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has led to the deprioritization of non-emergency services, such as oral food challenges and the initiation of oral immunotherapy (OIT) for food-allergic children. Recent studies have suggested that home-based peanut OIT could be a safe and effective option for low-risk peanut-allergic children. In the period between September 1, 2020, and January 31, 2021, nine preschoolers with a history of mild allergic reactions to peanut underwent home-based peanut OIT. Eight of them (88.9%) completed the build-up phase at home in 11-28 weeks, tolerating a daily maintenance dose of 320 mg peanut protein. During the build-up, six patients (75.0%) reported urticaria, three (33.3%) reported gastrointestinal tract symptoms, and one (14.3%) reported oral pruritis. None of the patients developed anaphylaxis, required epinephrine, or attended emergency services related to OIT. One or two virtual follow-up visits were completed per patient during the build-up phase. Our case series shows that home-based OIT could be offered to the low-risk preschoolers during the COVID-19 pandemic when non-emergency services are limited and could be considered beyond the pandemic, especially for the families living in the rural or remote areas that may otherwise be unable to access OIT.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".