National trends in emergency department visits and hospitalizations for food‐induced anaphylaxis in <scp>US</scp> children
Bibliographic record
Abstract
BACKGROUND: Food is the leading cause of anaphylaxis in children seen in emergency departments in the United States, yet data on emergency department visits and hospitalizations related to food-induced anaphylaxis are limited. The objective of our study was to examine national time trends of pediatric food-induced anaphylaxis-related emergency department visits and hospitalizations. METHODS: We conducted an observational study using a national administrative claims database from 2005 through 2014. Participants were younger than 18 years with an emergency department visit or hospitalization for food-induced anaphylaxis. Outcome measures of our study included time trends of pediatric food-induced anaphylaxis-related emergency department visits and hospitalizations, including observations (in an emergency department or a hospital unit), inpatient admissions, and intensive care unit admissions. RESULTS: During the study period, participants had 7310 food-induced anaphylaxis-related emergency department visits. Emergency department visits for food-induced anaphylaxis increased by 214% (P < .001); the highest rates were in infants and toddlers (age 0-2 years). Rates of emergency department visits significantly increased in all age-groups, with the highest increase in adolescents (age 13-17 years: 413%; P < .001). Peanuts accounted for the highest rates (5.85 per 100 000 in 2014) followed by tree nuts/seeds (4.62 per 100 000 in 2014). The greatest increase in rates of emergency department visits for food-induced anaphylaxis occurred with tree nuts/seeds (373.0% increase during the study period). CONCLUSIONS: The incidence of food-induced anaphylaxis has significantly increased over time in children of all ages. Food-induced anaphylaxis in children is an important national public health concern.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".