Peanut‐induced anaphylaxis in children and adolescents: Data from the European Anaphylaxis Registry
Bibliographic record
Abstract
BACKGROUND: Peanut allergy has a rising prevalence in high-income countries, affecting 0.5%-1.4% of children. This study aimed to better understand peanut anaphylaxis in comparison to anaphylaxis to other food triggers in European children and adolescents. METHODS: Data was sourced from the European Anaphylaxis Registry via an online questionnaire, after in-depth review of food-induced anaphylaxis cases in a tertiary paediatric allergy centre. RESULTS: 3514 cases of food anaphylaxis were reported between July 2007 - March 2018, 56% in patients younger than 18 years. Peanut anaphylaxis was recorded in 459 children and adolescents (85% of all peanut anaphylaxis cases). Previous reactions (42% vs. 38%; p = .001), asthma comorbidity (47% vs. 35%; p < .001), relevant cofactors (29% vs. 22%; p = .004) and biphasic reactions (10% vs. 4%; p = .001) were more commonly reported in peanut anaphylaxis. Most cases were labelled as severe anaphylaxis (Ring&Messmer grade III 65% vs. 56% and grade IV 1.1% vs. 0.9%; p = .001). Self-administration of intramuscular adrenaline was low (17% vs. 15%), professional adrenaline administration was higher in non-peanut food anaphylaxis (34% vs. 26%; p = .003). Hospitalization was higher for peanut anaphylaxis (67% vs. 54%; p = .004). CONCLUSIONS: The European Anaphylaxis Registry data confirmed peanut as one of the major causes of severe, potentially life-threatening allergic reactions in European children, with some characteristic features e.g., presence of asthma comorbidity and increased rate of biphasic reactions. Usage of intramuscular adrenaline as first-line treatment is low and needs to be improved. The Registry, designed as the largest database on anaphylaxis, allows continuous assessment of this condition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".