REMOVING MAN-MADE JUMPS FROM SNOW-PARKS REDUCES THE RISK OF SEVERE SKI-PATROL REPORTED INJURIES SUSTAINED BY SKIERS AND SNOWBOARDERS
Bibliographic record
Abstract
Background Evidence indicates that snow-park (SP) injuries are more severe than injuries on regular slopes. This prompted two major ski areas in the province of Québec, Canada, to remove all man-made jumps from their SPs before the 2007–2008 season. Objective To determine if removing jumps from SPs reduced the prevalence of severe injuries for skiers and snowboarders. Methods Subjects were skiers and snowboarders who reported to the ski patrol with a SP injury at two ski areas before (seasons 2000–2001 to 2006–2007) and after (seasons 2007–2008 to 2009–2010) SP jumps were removed, and for all the ski areas with no SP jump removal. Severe injuries were defined based on type of injury or ambulance evacuation. We compared the proportion of severe injuries before and after SP jump removal with trends at the other areas. Logistic regression analysis was used to adjust the pre- and post change comparison for age, sex, skill level, helmet use, and type of activity. Results At the two hills that removed jumps, the proportion of severe injuries was 19.3% (600 severe SP injuries/3109 all SP injuries) before and 14.5% (63/434) after the change compared with 23.7% (2679/11 324) and 22.1% (921/4173) at other hills. After covariate adjustment, the odds of severe injuries declined at the two areas that removed jumps (adjusted OR (AOR): 0.72; 95% CI 0.54 to 0.97) with no change at other Québec ski areas (AOR 0.98; 95% CI 0.83 to 1.08). Significance Results suggest that removing man-made jumps from SPs prevents severe injuries.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".