Feature-specific terrain park-injury rates and risk factors in snowboarders: a case–control study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Snowboarding is a popular albeit risky sport and terrain park (TP) injuries are more severe than regular slope injuries. TPs contain man-made features that facilitate aerial manoeuvres. The objectives of this study were to determine overall and feature-specific injury rates and the potential risk factors for TP injuries. METHODS: Case-control study with exposure estimation, conducted in an Alberta TP during two ski seasons. Cases were snowboarders injured in the TP who presented to ski patrol and/or local emergency departments. Controls were uninjured snowboarders in the same TP. κ Statistics were used to measure the reliability of reported risk factor information. Injury rates were calculated and adjusted logistic regression was used to calculate the feature-specific odds of injury. RESULTS: Overall, 333 cases and 1261 controls were enrolled. Reliability of risk factor information was κ>0.60 for 21/24 variables. The overall injury rate was 0.75/1000 runs. Rates were highest for jumps and half-pipe (both 2.56/1000 runs) and lowest for rails (0.43/1000 runs) and quarter-pipes (0.24/1000 runs). Compared with rails, there were increased odds of injury for half-pipe (OR 9.63; 95% CI 4.80 to 19.32), jumps (OR 4.29; 95% CI 2.72 to 6.76), mushroom (OR 2.30; 95% CI 1.20 to 4.41) and kickers (OR 1.99; 95% CI 1.27 to 3.12). CONCLUSIONS: Higher feature-specific injury rates and increased odds of injury were associated with features that promote aerial manoeuvres or a large drop to the ground. Further research is required to determine ways to increase snowboarder safety in the TP.
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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 it