THE EFFECT OF REMOVING MAN-MADE JUMPS FROM SNOW-PARKS ON 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 season 2007–08. Objective To determine if removing jumps from SPs reduced the prevalence of severe injuries for skiers and snowboarders. Design Retrospective study using ski-patrol Injury Report Forms (IRF). The proportion of severe injuries before (seasons 2000–01 to 2006–07) and after (seasons 2007–08 to 2009–10) SP jump removal was compared with proportions at other areas. Setting All ski areas in operation in the province (between 77 and 84). Participants Skiers and snowboarders who reported to the ski patrol with a SP injury at two ski areas before and after SP jumps were removed, and for all the ski areas with no SP jump removal. Risk factor assessment Risk factor data and injury outcomes were collected through IRFs. Main outcome measurements Severe injuries were defined based on type of injury or ambulance evacuation. The proportion of severe injuries before and after SP jump removal was compared with proportions at 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/3,109 all SP injuries) before and 14.5% (63/434) after the change, compared with 23.7% (2 679/11 324) and 22.1% (921/4 173) at other hills. The odds of severe injuries declined at the two areas that removed jumps (adjusted odds ratio [AOR]: 0.72; 95% CI: 0.54–0.97) with no change at other ski areas (AOR: 0.94; 95% CI: 0.83–1.08). Conclusions Results suggest that removing man-made jumps from SPs prevents severe injuries.
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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 | 0.004 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".