Injury and spatial epidemiology of severe adult trauma: implications for prevention
Bibliographic record
Abstract
Background Identifying who is injured within a geographic region, by what mechanism, is essential for injury prevention (IP). Our objective was to define the injury and spatial epidemiology of severe adult traumas to prioritise and target initiatives. Methods Epidemiologic profiles were generated for severely injured (ISS>12) adult (≥18 years) patients treated at Lead Trauma Hospitals (LTH) in Southwestern Ontario, 2004–2009. Sub-analysis was undertaken by age groups (18–24; 25–64; 65+ years). Injury cases were mapped by patient residence and place of injury to examine spatial relationships. Results LTHs resuscitated 2804 severely injured adults (15% young adult, 55% adult, 30% senior; 72% male). Patient residences were dispersed throughout SWO, with clusters in cities and lower-income areas. MVCs accounted for 61% and 46% of injuries among young adults and adults, respectively. Only 60% of injured-occupants wore a seatbelt; 24% of drivers had a BAC above the legal limit. MVCs were overly concentrated on high-density urban areas with highly mixed land uses. Alcohol was involved with nearly one-third of non-senior severe injury (48% of assaults; 34% of crashes). Falls were the leading injury mechanism for seniors (68%); 67% occurred at home. Only 6% of patients were injured at work, half involved falls. Mortality was 15%, with 42% fall-related deaths. Conclusion Integrating injury epidemiology with geographic data on patients daily surroundings allowed for the identification of socio-spatial variations in injury patterns among vulnerable groups. This approach identified MVCs, falls and alcohol use as IP priorities to be targeted to the populations and regions of greatest need.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".