Anaphylaxis-related deaths in Ontario: a retrospective review of cases from 1986 to 2011
Bibliographic record
Abstract
BACKGROUND: Examining deaths caused by anaphylaxis may help identify factors that may decrease the risk of these unfortunate events. However, information on fatal anaphylaxis is limited. The objectives of our study were to examine all cases of fatal anaphylaxis in Ontario to determine cause of death, associated features, co factors and trends in mortality. The identification of these factors is important for developing effective strategies to overcome gaps in monitoring and treatment of patients with food allergies and risk for anaphylaxis. METHODS: This was a retrospective case-series analysis of all causes of anaphylaxis-related deaths using data from the Ontario Coroner's database between 1986 and 2011. Quantitative data (e.g. demographic) were analyzed using descriptive statistics and frequency analysis using SPSS. Qualitative data were analyzed using content analysis of grounded theory methodology. RESULTS: We found 92 deaths in the last 26 years related to anaphylaxis. Causes of death, in order of decreasing frequency, included food (40 cases), insect venom (30 cases), iatrogenic (16 cases), and idiopathic (6 cases). Overall, there appears to be a decline in the frequency of food related deaths, but an increase in iatrogenic causes of fatalities. We found factors associated with fatal anaphylaxis included: delayed epinephrine administration, asthma, allergy to peanut, food ingestion outside the home, and teenagers with food allergies. CONCLUSIONS: Our findings indicate the need to improve epinephrine auto-injector use in acute reactions, particularly for teens and asthmatics with food allergies. In addition, education can be improved among food service workers and food industry in order to help food allergic patients avoid potentially fatal allergens. The increasing trend in iatrogenic related anaphylaxis is concerning, and requires monitoring and more investigation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".