Epidemiology of severe trauma among status Aboriginal Canadians: a population-based study
Bibliographic record
Abstract
BACKGROUND: Aboriginal Canadians are considered to be at increased risk of major trauma. However, population-based studies characterizing the distribution, determinants and outcomes of major trauma in this group are lacking. We sought to measure the impact of ethnicity, as reflected by Aboriginal status, on the incidence of severe trauma and to broadly define the epidemiologic characteristics of severe trauma among status Aboriginal Canadians in a large health region. METHODS: This population-based, observational study involves all adults (people > or = 16 years) resident in the Calgary Health Region between Apr. 1, 1999, and Mar. 31, 2002. Stratification of the population into status Aboriginal Canadians and the reference population was performed by Alberta Health and Wellness using an alternate premium arrangement field within the personal health care number. Injury incidence was determined by identifying all injuries with severity scores of 12 or greater in the Alberta Trauma Registry, regional corporate data and the Office of the Medical Examiner. RESULTS: Aboriginal Canadians were at much higher risk than the reference population in the Calgary Health Region of sustaining severe trauma (257.2 v. 68.8 per 100,000; relative risk [RR] 3.7, 95% confidence interval [CI] 3.0-4.6). Aboriginal Canadians were found to be at significantly increased risk of injuries resulting from motor vehicle crashes (RR 4.8, 95% CI 3.5-6.5), assault (RR 11.1, 95% CI 6.2-18.6) and traumatic suicide (RR 3.1, 95% CI 1.4-6.1). A trend toward higher median injury severity scores was observed among Aboriginal Canadians (21 v. 18, p = 0.09). Although the case-fatality rate among Aboriginal Canadians was less than half that in the reference population (14/93 [15%] v. 531/1686 [31%], p < 0.0001), population mortality was almost 2 times greater (RR = 1.8, 95% CI 1.0-3.0, p = 0.046). INTERPRETATION: Severe trauma disproportionately affects Aboriginal Canadians.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".