Canadians Reporting Sport-Related Concussions: Increasing and Now Stabilizing.
Bibliographic record
Abstract
BACKGROUND: Canada is currently engaged in a national harmonization of strategies to prevent and manage sport-related concussions. OBJECTIVE: To examine the annual incidence rates of reported sport-related concussions or other brain injuries by participants in the Canadian Community Health Survey, a national public health survey which provides nearly 2 decades of serial data using consistent methodology. DESIGN: Serial cross-sectional survey. SETTING: Population-based Canadian survey from 2000 to 2018 that collects data on "concussions or other brain injuries." PARTICIPANTS: Respondents 12 years and older. INDEPENDENT VARIABLES: Sex and age categorized 12 to 14 years, 15 to 19 years, 20 to 29 years, and 30+ years. OUTCOME MEASURES: National incidence rates of participants reporting concussions or other brain injuries occurring within the previous year while engaged in "organized sports/leisure sports or physical exercise." RESULTS: Data were available for 2000/01, 2003, 2005/6, 2009/10, 2013/4, and 2017/8 (N = 757 383). A previously stable annual incidence of reported sport-related concussions or other brain injuries increased nearly 2 and a half-fold from 2005/06 through 2013/14 (P < 0.0001) but seems to have stabilized recently (2013/14 vs 2017/8, P = 0.35). This trend is similar for both men and women but is manifest primarily within youth (12-19 years) as opposed to adults (>19 years). Approximately 1 in 450 Canadians 12 years and older report sport-related concussions or other brain injuries as their most significant injury associated with disability in the previous year (2017-2018: 221 per 100 000 population, 95% confidence interval: 179-264). CONCLUSIONS: In Canada, the annual incidence rates of reported sport-related concussions or other brain injuries is changing and may reflect improved reporting and recognition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".