Changing the history of anaphylaxis mortality statistics through the World Health Organization's International Classification of Diseases–11
Bibliographic record
Abstract
We review the history of the classification and coding changes for anaphylaxis and provide current and perspective information in the field. In 2012, an analysis of Brazilian data demonstrated undernotification of anaphylaxis-related deaths because of the difficulties of coding using the International Classification of Diseases, 10th Revision. This work triggered strategic international actions supported by the Joint Allergy Academies and the International Classification of Diseases World Health Organization (WHO) leadership to update the classification of allergic disorders for the International Classification of Diseases, 11th Revision (ICD-11), which resulted in construction of the pioneer "Allergic and hypersensitivity conditions" chapter. The usability of the new framework has been tested by evaluating the same data published in 2012 from the ICD-11 perspective. Coding accuracy was much improved, reaching 95% for definite anaphylaxis. As the results were provided to the WHO Mortality Reference Group, coding rules have been changed, allowing anaphylaxis to be recorded as an underlying cause of death in official mortality statistics. The mandatory use of ICD-11 from January 2022 for documenting cause of death could have 2 immediate consequences: (1) the reported number of anaphylaxis-related deaths might increase because of more appropriate coding and (2) the cross-sectional and longitudinal mortality data generated might ultimately lead to a better understanding of anaphylaxis epidemiology and improved health policies directed at reducing anaphylaxis-related mortality.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".