Epidemiology of severe anaphylaxis: can we use population-based data to understand anaphylaxis?
Bibliographic record
Abstract
PURPOSE OF REVIEW: The observed increase in incidence of allergic disease in many regions over the past 3 decades has intensified interest in understanding the epidemiology of severe allergic reactions. We discuss the issues in collecting and interpreting these data and highlight current deficiencies in the current methods of data gathering. RECENT FINDINGS: Anaphylaxis, as measured by hospital admission rates, is not uncommon and has increased in the United Kingdom, the United States, Canada, and Australia over the last 10-20 years. All large datasets are hampered by a large proportion of uncoded, 'unspecified' causes of anaphylaxis. Fatal anaphylaxis remains a rare event, but appears to be increasing for medication in Australia, Canada, and the United States. The rate of fatal food anaphylaxis is stable in the United Kingdom and the United States, but has increased in Australia. The age distribution for fatal food anaphylaxis is different to other causes, with data suggesting an age-related predisposition to fatal outcomes in teenagers and adults to the fourth decade of life. SUMMARY: The increasing rates of food and medication allergy (the latter exacerbated by an ageing population) has significant implications for future fatality trends. An improved ability to accurately gather and analyse population-level anaphylaxis data in a harmonized fashion is required, so as to ultimately minimize risk and improve 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".