Snowboarding Injuries in Australia: Investigating Risk Factors in Wrist Fractures to Enhance Injury Prevention Strategies
Bibliographic record
Abstract
OBJECTIVE: To investigate risk factors associated with wrist fractures in snowboarders to inform future snowsport safety strategies. METHODS: A prospective case-control study using a nonprobability convenience sample was conducted with data collected via a respondent-completed questionnaire. Subjects consisted of snowboarders with a snowboard-related injury who presented to one of 10 medical centers and physiotherapy clinics in resort medical centers and gateway communities across the Australian snowsport season in 2007. Those presenting with injuries other than wrist fractures acted as the control. RESULTS: The 611 respondents reported 802 injuries (61.3% were males and 51.5% were aged 16-25 years). Protective equipment was worn by 57.0% of respondents. The main reason for not wearing a wrist guard was that they did not see the need; of these, 12.9% experienced a wrist fracture. Most injuries occurred on-piste, in a terrain park, or in a lesson. The main mechanism of injury was falling. The major risk factors for wrist fractures were being less than 16 years of age (OR 3.97, CI 2.54-6.22), being in the alpine area for a holiday (OR 2.77, CI 1.47-5.21), and being a first-day snowboard participant (OR 2.02, CI 1.15-3.64). A direct logistic regression indicated that 3 variables had a statistically significant contribution to the model (being less than 16 years old, being on holidays in the region, and not wearing a wrist guard). CONCLUSIONS: The key risk factors in this Australian study reflect other international studies, providing a clear market segment for targeted snowsport safety messages: those less than 16 years old, visitors to the alpine regions, and those not wearing wrist guards.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".