Risk of peanut- and tree-nut–induced anaphylaxis during Halloween, Easter and other cultural holidays in Canadian children
Bibliographic record
Abstract
BACKGROUND: It is not established whether the risk of anaphylaxis induced by peanuts or tree nuts in children increases at specific times of the year. We aimed to evaluate the risk of peanut-and tree-nut-induced anaphylaxis during certain cultural holidays in Canadian children. METHODS: We collected data on confirmed pediatric cases of anaphylaxis presenting to emergency departments in 4 Canadian provinces as part of the Cross-Canada Anaphylaxis Registry. We assessed the mean number of cases per day and incidence rate ratio (IRR) of anaphylaxis induced by unknown nuts, peanuts and tree nuts presenting during each of 6 holidays (Halloween, Christmas, Easter, Diwali, Chinese New Year and Eid al-Adha) versus the rest of the year. We estimated IRRs and 95% confidence intervals (CIs) using Poisson regression. RESULTS: Data were collected for 1390 pediatric cases of anaphylaxis between 2011 and 2020. Their median age was 5.4 years, and 864 (62.2%) of the children were boys. During Halloween and Easter, there were higher rates of anaphylaxis to unknown nuts (IRR 1.66, 95% CI 1.13-2.43 and IRR 1.71, 95% CI 1.21-2.42, respectively) and peanuts (IRR 1.86, 95% CI 1.12-3.11 and IRR 1.57, 95% CI 0.94-2.63, respectively) compared to the rest of the year. No increased risk of peanut- or tree-nut-induced anaphylaxis was observed during Christmas, Diwali, Chinese New Year or Eid al-Adha. Anaphylaxis induced by unknown nuts, peanuts and tree nuts was more likely in children aged 6 years or older than in younger children. INTERPRETATION: We found an increased risk of anaphylaxis induced by unknown nuts and peanuts during Halloween and Easter among Canadian children. Educational tools are needed to increase awareness and vigilance in order to decrease the risk of anaphylaxis induced by peanuts and tree nuts in children during these holidays.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".