Trends in self-reported traumatic brain injury among Canadians, 2005-2014: a repeated cross-sectional analysis
Bibliographic record
Abstract
Background: Concussion and other traumatic brain injuries (TBIs) are a form of unintentional injury that has been associated with both short- and long-term health effects, including possible disability. We investigated time trends in the incidence of all types of injury and TBIs among Canadians, and assessed characteristics of TBIs. Methods: We used data from annual cycles of the Canadian Community Health Survey, 2005 to 2014, to examine all types of injury and TBI among Canadians aged 12 years or more. We estimated TBI incidence among respondents who reported any type of injury in the previous year. We used descriptive methods to describe key characteristics (sex, age, season, activity and venue) and 5- and 10-year trends, and generalized linear models to estimate annual percent change in the incidence of all types of injury and TBI. Results: The incidence of all types of injury and of TBIs increased between 2005 and 2014, with an annual percent change of 1.4 (95% confidence interval [CI] 0.9-1.9) and 9.6 (95% CI 8.2-11.0), respectively. Sport venues (39.9% [95% CI 32.7-47.1)] and sports-related activities (49.7% [95% CI 42.4-57.0]) were commonly associated with TBIs, and falls were the most frequent mechanism of injury (53.9% [95% CI 46.7-61.0]) leading to a TBI. Interpretation: Our findings highlight the increasing trends in all types of injury and TBIs in Canada, and underscore the need for ongoing population level surveillance and targeted prevention efforts to mitigate risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".