Comparing Two Peanut Desensitization Protocols in Preschool Children: A Real-World Clinical Practice
Bibliographic record
Abstract
INTRODUCTION: Peanut allergy is the main food allergy in childhood and poses significant health concerns. This study aimed to critically evaluate the effectiveness and safety of oral immune therapy (OIT) using crushed peanuts versus peanut puffs. METHODS: Children with an allergist diagnosed peanut allergy based on a history of an IgE-mediated reaction and a positive skin prick test for peanuts were recruited at the Montreal Children's Hospital and the Children's Clinic located in Montreal. Based on age and personal preference, initial doses of peanut were given in either puff (Bamba) or crushed peanut form. Patients continued the same dose for 2-5 weeks at home, filled out a symptom diary, and returned to the clinic for up-dosing until maintenance was reached (2 teaspoons of peanut butter). A continuation ratio regression model was used to evaluate the effect of the allergen type on the severity of anaphylactic and allergic reactions (ARs) during OIT while adjusting for potential confounders. RESULTS: Between October 2020 and June 2023, 191 children (59.6% male; median age 1.95 years) were recruited. Most patients (75.1%) had eczema, and 12.7% had asthma. Oral desensitization was performed using one of two strategies according to the allergist: crushed peanut (n = 60 [31.4%]) and peanut puff (n = 131 [68.6%]). Of the participants, the consumption of puff lowered reaction severity by a factor of 3.94 (95% CI, 1.6-9.6), in comparison to crushed peanuts. Older age markedly elevates the adjusted odds of reacting to a particular severity level as compared to a lower level by 1.20 (95% CI, 1-1.4). CONCLUSION: Modified peanut desensitization using peanut puffs has shown potential in reducing the severity of ARs in younger children. Older children may experience a higher risk of severe reactions, indicating the need for age-specific approaches to desensitization protocols.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".