Trauma in northern Quebec, 2005–2014: epidemiologic features, transfers and patient outcomes
Bibliographic record
Abstract
BACKGROUND: The Inuit people residing in Nunavik, Quebec, are vulnerable to major trauma owing to environmental and social factors; however, there is no systematic data collection for trauma in Nunavik, and, apart from data regarding patients who are transferred to tertiary care centres, no data enter the Quebec trauma registry directly from Nunavik. We performed a study to characterize the epidemiologic features of trauma in Nunavik, and describe indications for transfer and outcomes of patients referred to the tertiary trauma centre. METHODS: We collected data retrospectively for all patients with trauma admitted to the Centre de santé Tulattavik de l'Ungava in Kuujjuaq from 2005 to 2014. Sociodemographic, injury and health services data were extracted. The data were analyzed in conjunction with coroners' reports on death from trauma in Nunavik. RESULTS: A total of 797 trauma cases were identified. The most common causes of injury were motor vehicle collisions (258 cases [32.4%]), falls (137 [17.2%]) and blunt assault (95 [11.9%]). One-third of patients (262 [32.9%]) were transferred to the tertiary care centre in Montréal. The incidence rate of major trauma (Injury Severity Score > 12) was 18.1 and 21.7 per 10 000 person-years in the Kuujjuaq region and the Puvirnituq region, respectively, which translates to a relative risk (RR) of 4 compared to the Quebec population. The disparity observed in trauma mortality rate was even greater, with an RR of 47.6 compared to the Quebec population. CONCLUSION: The study showed major disparity in trauma incidence and mortality rate between Nunavik and the province of Quebec. Our findings allow for a better understanding of the burden of injury and regional trauma mortality in Nunavik, and recommendations for optimization of the trauma system in this unique setting.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".