MAN-MADE JUMPS IN TERRAIN-PARKS AND THE RISK OF SEVERE SKI-PATROL REPORTED INJURIES IN ALPINE SKIERS AND SNOWBOARDERS
Bibliographic record
Abstract
Background Two ski areas in Québec, Canada, removed man-made jumps from their terrain parks (TPs) from seasons 2007–08 to 2009–10. Jumps were reintroduced starting in season 2010–11. Objective To determine if man-made jumps in TPs are associated with severe alpine ski and snowboard injuries. Design Retrospective study using ski-patrol Injury Report Forms (IRFs). Setting All ski areas with a TP in Québec. Patients (or Participants) Skiers and snowboarders who reported to the ski patrol with a TP injury during seasons 2000–01 to 2014–15, at all Québec ski areas with TP (two where jumps were removed, and others without jump removal). Interventions (or Assessment of Risk Factors) 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 proportions of severe injuries before jump removal (2000–01 to 2006–07) and during jump reintroduction (2010–11 to 2014–15) were compared with proportions during jump removal (2007–08 to 2009–10). Logistic regression analysis was used to adjust the comparisons for age, sex, skill level, helmet use, and type of activity. Generalized Estimating Equations were used to account for potential ski area clustering of outcomes. Results At the hills that removed jumps (4,006 IRFs analysed), the crude proportion of severe TP injuries was 19.3% before removal, 14.5% during removal, and 24.3% at reintroduction. The odds of severe injury were higher before removal (adjusted odds ratio [AOR]: 1.39; 95% CI: 1.03–1.87) and during reintroduction (AOR: 1.90; 95% CI: 1.52–2.37). At other hills over the same time period (21,449 IRFs), there was no change before (23.7%) and during (22.0%) jump removal (AOR: 1.07; 95% CI: 0.94–1.21), but the proportion of severe injuries increased after reintroduction (26.4%; AOR: 1.26; 95% CI: 1.10–1.43). Conclusions Results suggest that removing man-made jumps from TPs may prevent 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".