Injuries at the Whistler Sliding Center: a 4-year retrospective study
Bibliographic record
Abstract
BACKGROUND: The Whistler Sliding Centre (WSC) in British Columbia, Canada, has played host to many events including the 2010 Winter Olympics. This study was performed to better understand sliding sport incident (crash, coming off sled, etc) and injury prevalence and provide novel insights into the effect of slider experience and track-specific influences on injury risk and severity. METHODS: Track documentation and medical records over 4 years (2007 track inception to 2011) were used to form 3 databases, including over 43,200 runs (all sliding disciplines). Statistics were generated relating incident and injury to start location, crash location and slider experience as well as to understand injury characteristics. RESULTS: Overall injury rate was found to be 0.5%, with more severe injury occurring in <0.1% of the total number of runs. More frequent and severe injuries were observed at lower track locations. Of 2605 different sliders, 73.6% performed 1-29 runs down the track. Increased slider experience was generally found to reduce the frequency of injury. Lacerations, abrasions and contusions represented 52% of all injuries. A fatality represented the most severe injury on the track and was the result of track ejection. CONCLUSIONS: By investigating the influence of start location, incident location and slider experience on incident and injury frequency and severity, a better understanding has been achieved of the inherent risks involved in sliding sports. Incident monitoring, with particular focus on track ejection, should be an emphasis of sliding tracks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".