Trends of Peanut-Induced Anaphylaxis Rates Before and After the 2017 Early Peanut Introduction Guidelines in Montreal, Canada
Bibliographic record
Abstract
BACKGROUND: Food allergies, particularly peanut, represent the predominant cause of anaphylaxis. Whereas early allergen introduction has emerged as a potential preventive strategy, the precise impact of recent guidelines on peanut-induced anaphylaxis rates in Canada remains unclear. OBJECTIVE: To assess the impact of the 2017 Addendum Guidelines for the Prevention of Peanut Allergy on peanut-induced anaphylaxis rates in Canada. METHODS: Using a comprehensive longitudinal registry capturing pediatric anaphylaxis presentations to the Montreal's Children's Hospital, we compared children with and without known peanut allergy who presented with peanut-induced anaphylaxis between 2011 and 2019 inclusive, excluding data beyond 2019 owing to the Coronavirus disease 2019 (COVID-19) pandemic. We calculated rates of peanut-induced anaphylaxis presentations per 100,000 age-adjusted all-cause emergency department visits using 4-month intervals. Interrupted time series analysis was used to compare anaphylaxis rate trends before and after 2017 for children ages 0 to 2 and 3 to 17 years. RESULTS: We examined 2,011 cases of pediatric anaphylaxis, including 429 (21%) triggered by peanuts. Compared with pre-guideline estimates, the yearly rate of change of peanut anaphylaxis rates decreased by 7.96 (95% confidence interval -14.57 to -1.36; P = .018) after 2017 among patients with new-onset anaphylaxis in children 2 years of age or younger (n = 109). No significant changes were identified for older patients ages 3 to 17, or in patients with known peanut allergy. CONCLUSIONS: Early introduction guidelines in Canada are associated with a reduced risk of new-onset peanut-induced anaphylaxis in young children within a single center in Montreal. Further research is required to assess the impact on a wider population and other food allergens.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".