Global patterns of drug allergy-induced fatalities: a wake-up call to prevent avoidable deaths
Bibliographic record
Abstract
PURPOSE OF REVIEW: To identify patterns and key issues though a systematic review in order to support prevention strategies and reduce avoidable deaths related to drug-induced anaphylaxis (DAF). RECENT FINDINGS: DAF rate has been estimated by 0.13-0.53/106 population/year. General global trends of DAF are increasing over time, mostly occurring at healthcare settings (62%) with a similar gender distribution and an average age of 53 years. Antibiotics, anaesthetics, radio-contrast media and NSAIDs were the most frequently implicated agents. Main comorbidities were personal history of drug allergy, cardiovascular diseases and asthma. Main manifestations were cardiovascular and respiratory commitments. Use of adrenaline is mentioned in only 29% of the articles. SUMMARY: DAF is increasing worldwide and most cases are iatrogenic. This first systematic review of DAF identified key gaps and served as a wake-up call to prevent avoidable deaths. Phenotype at risk for DAF was represented by patients aged more than 54 years, with personal history of drug allergy/hypersensitivity with no or incomplete allergological work-up, cardiovascular disease and/or asthma with need of hospitalization and/or frequent healthcare assistance. Additional risk for those who need frequent use of intravenous antibiotics and/or undergoing surgery or image investigation with radiocontrast media.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".