What are the risk factors for injuries and injury prevention strategies for skiers and snowboarders in terrain parks and half-pipes? A systematic review
Bibliographic record
Abstract
OBJECTIVE: To synthesise the current evidence regarding the risk factors, the injury prevention strategies and the profile of injured skiers and snowboarders in terrain parks (TPs) and half-pipes (HPs). DESIGN: Systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. DATA SOURCES: Literature searches from six electronic databases and manual searches were performed. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Inclusion criteria were: (1) publication based on original data; (2) injuries sustained in TPs or HPs; (3) recreational skiing or snowboarding injuries; (4) observational or experimental study design with a comparison group. RESULTS: No study explored the risk factors in HPs or the prevention strategies in TPs or HPs. From the literature retrieved, there is strong evidence that skiing or snowboarding in a TP is a risk factor for head, neck, back and severe injuries. Two papers assessed the risk factors for injuries in TPs, mainly demonstrating that features promoting aerial manoeuvres or a large drop to the ground were associated with higher feature-specific injury rates. The profile of injured skiers and snowboarders in TPs described in the literature suggested some evidence of associations between factors including activity, sex, skill level, helmet use, age and TP injuries. SUMMARY/CONCLUSIONS: This systematic review demonstrates the need for studies identifying the risk factors for injuries to skiers and snowboarders and on interventions to reduce the risk of injury in TPs and HPs. Studies addressing the issue of TP design should be considered. PROSPERO REGISTRATION NUMBER: CRD42016045206.
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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 | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".