Injuries Among Skiers and Snowboarders in Quebec
Bibliographic record
Abstract
BACKGROUND: Snow sports such as skiing and snowboarding are recognized as hazardous, but population-based injury rates or specific risk factors have been difficult to estimate as a result of a lack of complete data for both numerator and denominator. METHODS: We used data from 3 surveys to estimate the number of participants and annual number of outings in Quebec by age, sex, activity, and calendar year. Injuries reported by ski patrollers were used to estimate injury rates among skiers and snowboarders for the head and neck, trunk, upper extremity, and lower extremity. RESULTS: Head-neck and trunk injury rates increased over time from 1995-1996 to 1999-2000. There was a steady increase in the rate of injury with younger age for all body regions. The rate of head-neck injury was 50% higher in snowboarders than in skiers (adjusted rate ratio [ARR] = 1.5; 95% confidence interval = 1.3-1.8). Women and girls had a lower rate of head-neck injury (0.73; 0.62-0.87). Snowboarders were twice as likely as skiers to have injuries of the trunk (2.1; 1.7-2.6), and more than 3 times as likely to have injuries of the upper extremities (3.4; 2.9-4.1). Snowboarders had a lower rate of injury only of the lower extremities (0.79; 0.66-0.95). Snowboarder collision-related injury rates increased substantially over time. CONCLUSIONS: Except for lower extremity injuries, snowboarders have a higher rate of injuries than skiers. Furthermore, collision-related injury rates have increased over time for snowboarders. Targeted injury prevention strategies in this group seem justified.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".