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. These populations rely on large jurisdiction surveillance efforts to inform their prevention strategies, which may not address their needs. We examined the incidence and determinants of traumatic brain injury in an indigenous population in the Terres-Cries-de-la-Baie-James health region of the province of Quebec and compared them with the incidence and determinants in 2 neighbouring health regions and in the province overall. METHODS: We conducted a retrospective population-based cohort study of patients in Quebec admitted to hospital with incident traumatic brain injury, stratified by health region (Terres-Cries-de-la-Baie-James, Nunavik and Nord-du-Québec), from 2000 to 2012. We used MED-ÉCHO administrative data for case-finding. A subgroup analysis of adults in the Terres-Cries-de-la-Baie-James health region was completed to assess determinants of the severity of traumatic brain injury and patient outcomes. RESULTS: A total of 172 hospital admissions for incident traumatic brain injury occurred in the Terres-Cries-de-la-Baie-James region during the study period. The incidence was 92.1 per 100 000 person-years, and the adjusted incidence rate ratio was 1.84 (95% confidence interval 1.56-2.17) compared with the entire province. The incidence was higher than in the neighbouring nonindigenous population (Nord-du-Québec) but significantly lower than in the neighbouring indigenous population (Nunavik). Determinants of traumatic brain injury in the Terres-Cries-de-la-Baie-James region differed from those in the neighbouring populations and in the entire province. INTERPRETATION: We found that the incidence rates and determinants of traumatic brain injury requiring hospital admission varied greatly between the three regions studied. Community-based surveillance efforts should be encouraged to inform the development of relevant 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.001 | 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".