739 Epidemiology of Accidental Burn Injuries: National Trends in Hospitalizations and Emergency Department Visits (2017-2022)
Bibliographic record
Abstract
Abstract Introduction Understanding the evolving epidemiological trends of burns is essential for informing prevention strategies and healthcare resource allocation. While previous studies have described these trends within specific provinces, a comprehensive analysis of country-wide data is lacking. Methods We conducted a national retrospective population-based study of all burn-related emergency department (ED) visits and hospitalizations over a 5-year period (2017-2022). Data were extracted from a national repository of health administrative data. Injuries due to assault and suicide attempt were not included. Results Over a 5-year period there were 13,795 burn-related hospitalizations. There were four burn injury mechanisms identified in the database, from least to most common being related to electric current, explosion, fire and flame, and hot substances. The incidence of burns requiring hospitalization in the country remained constant over the 5-year period at 0.007%, though the absolute number of admissions generally increased over time. ED visit data were only available for 6 provinces in which there has been an overall decline in burn-related ED visits. The overall incidence of burns presenting to the ED decreased from 0.13% to 0.12% over the study period. Furthermore, the proportion of ED visits requiring hospitalization has increased over time from 5.4% to 6.4%. Conclusions In summary, this national population-based study of burn injuries from 2017-2022 has demonstrated that while the incidence of burn-related ED visits in several provinces has decreased, the national incidence of burn-related hospitalizations in the country has remained stable. These findings shed light on the changing landscape of burn injuries and underscore the need for continued monitoring and targeted prevention strategies. Applicability of Research to Practice This study offers important insights into the epidemiology of burn injuries from 2017-2022 and provides a foundation for evidence-based decision-making in burn injury resource allocation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".