Injuries and terrain park feature use among snowboarders in alberta
Bibliographic record
Abstract
Background Snowboarding is a popular and risky winter sport. Snowboarders perform tricks on man-made features in terrain parks, which may introduce additional risk. Objective To determine snowboard terrain park feature-specific injury rates and risk factors. Design Case-control study with exposure estimation. Setting A terrain park at a resort in Alberta, Canada, used for recreational and competitive snowboarding. Participants Cases were snowboarders injured in the terrain park who presented to the ski patrol or local emergency department (ED) (n=334). Controls were non-injured snowboarders using the terrain park (n=1262). The number of snowboarder-runs in the terrain park was recorded. Participants were recruited for two winter seasons. Assessment of risk factors Cases were identified from resort patient care records (PCRs) and ED logs. The PCRs captured demographic and environmental risk factors and injury assessment. Injured snowboarders were telephoned to determine exposure (feature used), listening to music and drugs/alcohol. Randomly selected controls were interviewed. Main outcome measurements Overall and feature-specific injury rates (per 1000 runs) were calculated. Cases and controls were compared for risk factor prevalence using multiple logistic regressions to estimate adjusted OR (aOR) and 95% CI. Results The overall injury rate was 0.75 injuries/1000 runs. Injury rates were highest on jumps (2.56/1000 runs), the half-pipe (2.56/1000 runs) and kickers (0.61/1000 runs). Compared with rails, the adjusted odds of injury were significantly higher on the half-pipe (aOR=9.6; 95% CI 4.8 to 19.3), jumps (aOR=4.3; 95% CI 2.7 to 6.8), mushroom (aOR=2.3; 95% CI 1.1 to 4.4) and kickers (aOR=2.0; 95% CI 1.3 to 3.1). The odds of severe injury (present to ED) versus minor injury did not differ by feature. Conclusions The injury rates and odds of injury were highest on features that facilitate aerial maneouvers. Resorts may consider marking all features to indicate difficulty and associated injury risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".