Equestrian-related brain injuries presenting to emergency departments, Canada, 1990-2014
Bibliographic record
Abstract
Background: Horse riding is a hazardous activity with the potential for serious injury. Equestrian-related injuries account for a higher rate of injury per number of riding hours than motorcyclists and automobile racers. There is a lack of literature pertaining to equestrian-related brain injuries. The objectives of this study were to describe the incidence, characteristics, and mechanisms of equestrian-related brain injuries sustained amongst Canadians between 1990 and 2014. Methods: Data were obtained from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) database. The study population consisted of individuals who sustained equestrian-related brain injuries between the years 1990 and 2014 and presented to one of 15 participating emergency departments. Results: Brain injuries accounted for 13.3% (N=1060) of all equestrian-related injuries. The greatest proportion of injuries occurred amongst individuals aged 15-19 years, followed by individuals aged 0-4 years. The predominant mechanism of injury was falls. 17.9% of individuals were admitted to hospital. Normalized rates of injury increased from 1990 to 2010. Conclusions: Brain injuries sustained while participating in equestrian are often of a greater severity than injuries sustained while participating in other recreational activities. A clear understanding of the epidemiology and mechanisms of equestrian-related brain injuries must be achieved in order to effectively implement prevention efforts.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".