The hidden epidemiology of trauma in Nunavik: a call for a dedicated trauma registry
Bibliographic record
Abstract
Background: Delivering trauma care in Nunavik is challenging. Despite the benefits of trauma registries, no routine data collection captures data from Nunavik patients in Quebec’s provincial database. We sought to compare trauma epidemiology from data collected on site in Nunavik with data from a governmental registry at a tertiary centre, hypothesizing sufficient cohort differences to justify a dedicated registry. Methods: We conducted a retrospective review of 2 cohorts (2015 to 2019). The first cohort included patients at Kuujjuaq’s Centre de santé Tulattavik de l’Ungava or Puvirnituq’s Centre de santé Inuulitisivik (the Nunavik cohort) and the second cohort included patients admitted to the provincial referral centre for the Nunavimmiut at the Montreal General Hospital (MGH). Nunavik data were collected through chart review, while MGH data were obtained from the McGill University Health Centre Trauma Registry. We analyzed patient demographics, injury mechanisms, transfer characteristics, and modifiable risk factors using descriptive statistics. Results: We identified 776 patients in the Nunavik cohort, of whom 42.0% were transferred to the MGH. Of all 776 trauma patients in Nunavik, only 14.3% were captured in the trauma registry. Among those transferred to the MGH, 33.9% were recorded in the registry, highlighting a gap in data representation. Patients in the Nunavik cohort were significantly younger (30 yr v. 37 yr, p < 0.001) and more often female (51.0% v. 38.8%, p < 0.001). Mechanisms of injury and vital signs also differed significantly (p < 0.001). Conclusion: Data from many patients from Nunavik are not captured in the governmental database, with the trauma epidemiology in the region significantly differing from those presenting to the tertiary centre. A dedicated prospective, sustainable registry is needed to improve quality of care and outcomes in Nunavik.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".