Inadvertent exposures in children with peanut allergy
Bibliographic record
Abstract
OBJECTIVES: To determine the annual incidence, characterize the severity and management, and identify predictors of accidental exposure among a cohort of children with peanut allergy. METHODS: From 2004 to November 2009, parents of Canadian children with a physician-confirmed peanut allergy completed entry and follow-up questionnaires about accidental exposures over the preceding year. Logistic regression analyses were used to examine potential predictors. RESULTS: A total of 1411 children [61.3% boys, mean age 7.1 yr (SD, 3.9)] participated. When all children were included, regardless of length of observation, 266 accidental exposures occurred over 2227 patient-years, yielding an annual incidence rate of 11.9% (95% CI, 10.6-13.5). When all accidental exposures occurring after study entry and patients providing <1 yr of observation were excluded, 147 exposures occurred over a period of 1175 patient-years, yielding a rate of 12.5% (95% CI, 10.7-14.5). Only 21% of moderate and severe reactions were treated with epinephrine. Age ≥13 yr at study entry (OR, 2.33; 95% CI, 1.20-4.53) and a severe previous reaction to peanut (OR, 2.04; 95% CI, 1.44-2.91) were associated with an increased risk of accidental exposure, and increasing disease duration (OR, 0.88; 95% CI, 0.83-0.92) with a decreased risk. CONCLUSION: The annual incidence rate of accidental exposure for children with peanut allergy is 12.5%. Children with a recent diagnosis and adolescents are at higher risk. Hence, education of allergic children and their families is crucial immediately after diagnosis and during adolescence. As many reactions were treated inappropriately, healthcare professionals require better education on anaphylaxis management.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".