Critical view of anaphylaxis epidemiology: open questions and new perspectives
Bibliographic record
Abstract
In contrast to the majority of allergic or hypersensitivity conditions, worldwide anaphylaxis epidemiological data remain sparse with low accuracy, which hampers comparable morbidity statistics. Data can differ widely depending on a number of variables. In the current document we reviewed the forms on which anaphylaxis has been defined and classified; and how it can affect epidemiological data. With regards to the methods used to capture morbidity statistics, we observed the impact of the anaphylaxis coding utilizing the World Health Organization's International Classification of Diseases. As an outcome and depending on the anaphylaxis definition, we extracted the cumulative incidence, which may not reflect the real number of new cases. The new ICD-11 anaphylaxis subsection developments and critical view of morbidity statistics data are discussed in order to reach new perspectives on anaphylaxis epidemiology.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".