A population-based epidemiological study of anaphylaxis using national big data in Korea: trends in age-specific prevalence and epinephrine use in 2010–2014
Bibliographic record
Abstract
BACKGROUND: Previous reports on anaphylaxis in Asia are limited to relatively small-scale studies. We performed this study to identify the nationwide prevalence of anaphylaxis and epinephrine prescription rates by age groups. METHODS: The total number of patients, yearly and overall prevalence, percentage of emergency department visits, and epinephrine prescription rates were calculated for patients diagnosed with anaphylaxis based on the Korean National Health Insurance database from 2010 to 2014. RESULTS: The mean prevalence of anaphylaxis in Korea was 26.23 (95% confidence interval, CI 25.78-26.68) per 100,000 person-years during the 5 years. It increased from 20.55 (95% CI 20.15-20.10) in 2010 to 35.33 (95% CI 34.81-35.85) per 100,000 person-years in 2014. The average prevalence was > 35 per 100,000 person-years among 50-69 year-olds, and the mean crude prevalence in children was 22.3 (0-2 years), 17.3 (3-6 years), 12.1 (7-12 years), and 14.9 (13-17 years) per 100,000 person-years, respectively. The overall prevalence increased 1.7-fold, with the highest rate of increase in 0-2 years of age. The overall percentage of emergent anaphylaxis patients was 88.4%, and the prevalence of emergent anaphylaxis increased from 18.63 (95% CI 18.25-19.01) to 31.28 (95% CI 30.79-31.77) per 100,000 person-years. In-hospital epinephrine prescription rate increased from 31.5 to 39.7%. CONCLUSIONS: The mean prevalence of anaphylaxis in Korea was 26.2 per 100,000 person-years during the study period. The total number of anaphylaxis patients increased 1.7-fold from 2010 to 2014, with the most noticeable increment being in young children.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".