P.080 Traumatic brain injury in a rural indigenous population in Canada: a community-based approach to surveillance
Bibliographic record
Abstract
Background: Indigenous populations are disproportionately affected by traumatic brain injury (TBI). These populations rely on large jurisdiction surveillance efforts to inform their prevention strategies, which may not address their needs. This study describes the TBI determinants of a Quebec indigenous population, the Cree served by the Terres-Cries-de-la-Baie-James health region, and compares them to the determinants of two neighbouring health regions and the entire Province of Quebec. Methods: We conducted a retrospective population-based cohort study of incident TBI hospitalizations, stratified by the aforementioned health regions, in Quebec from 2000-2012. MED-éCHO administrative data were used for case finding. A sub-analysis of the Terres-Cries-de-la-Baie-James adults was completed to assess for determinants of TBI severity and outcomes. Regression models, multiple imputations and a sensitivity analysis were used to account for biased associations. Results: 172 incident TBI hospitalizations occurred in the Terres-Cries-de-la-Baie-James region from 2000-2012. The incidence rate was 92.1 per 100,000 person-years and the adjusted IRR was 1.86 (95% CI 1.56-2.17) when compared to the entire province. Determinants of TBI for the Terres-Cries-de-la-Baie-James were significantly different from those of neighboring populations and the entire province. Conclusions: TBI surveillance information from large jurisdiction initiatives can be misleading for indigenous communities. Community-based surveillance provides evidence that these populations should use to prioritize prevention strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".