292 Patterns of Burn Injuries in Winnipeg, Manitoba: A Population Health Study
Bibliographic record
Abstract
Burn injuries are a leading cause of morbidity around the world. A more targeted approach towards high risk groups would reduce the burn incidence in a cost effective manner. Poverty has often been associated with an increased risk of burn injuries in countries like UK, Australia and USA. We describe the sociodemographics of burn survivors in Winnipeg, Manitoba, Canada. This retrospective review included adult burn survivors, requiring more than one day of hospitalization our local burn referral centre from 2006 to 2016. Patient resident postal codes were used. Participants were divided into eight Federal electoral districts using the Statistics Canada data. Population count and median household income for electoral districts was obtained from the 2011 Canada Census. Participants were also divided at the Census Tract (CT) level based on 2011 census.CTs were grouped together into quintiles based on the median household income for each CT. Arc GIS software was used for mapping Hot spot analysis using Getis-Ord Gi* was used to identify hotspots of increased burn incidence. Burn incidence was calculated for each income quintile. The overall incidence of severe burns in Winnipeg was 3.60 per 100,000 adults per year. The incidence varies across the city, ranging from 1.65 in Winnipeg South to 7.78 in Winnipeg Centre; a relative risk rate of 4.7. Burn incidence is inversely related (r = 0.72) to median household income quintile at the census tract level.At the census tract level, the burn incidence decreases incrementally for each quintile as median household income increases. There is a clear association between burn injury and median income; lower income neighbourhoods in Winnipeg have a higher incidence of burns. Burn prevention strategies in Winnipeg should target areas like Winnipeg Centre and Winnipeg North, which have more than five-fold higher incidence of burns than other areas. Geographical variation in income and affordability of equipment, such as fire alarms and fire extinguishers, should also be a factor when implementing burn prevention policies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".