Mountain Biking Injuries Requiring Trauma Center Admission: A 10-year Regional Trauma System Experience
Bibliographic record
Abstract
BACKGROUND: Mountain biking has become an increasingly popular recreational and competitive sport with increasingly recognized risks. The purpose of this study was to review a population based approach to serious injuries requiring trauma center admission related to mountain biking, identify trends and develop directions for related injury prevention programs. METHODS: Three trauma centers in the Greater Vancouver area exclusively serve a major mountain bike park and the North Shore Mountains biking trails. The Trauma Registries and the patient charts were reviewed for mountain bike injuries from 1992 to 2002. The data were analyzed according to demographics, distribution, and severity of injuries, and need for operative intervention. Findings were reviewed with injury prevention experts and regional and national mountain-biking stakeholders to provide direction to injury prevention programs. RESULTS: A total of 1,037 patients were identified as having bicycling-related injuries. Of these, 399 patients sustained 1,092 injuries while mountain biking. There was a threefold increase in the incidence of mountain biking injuries over a 10-year period. Young males were most commonly affected. Orthopedic injuries were most common (46.5%) followed by head (12.2%), spine (12%), chest (10.3%), facial (10.2%), abdominal (5.4%), genitourinary (2.2%), and neck injuries (1%). High operative rate was observed: 38% of injuries and 66% of patients required surgery. One patient died from his injuries. Injury prevention programs were developed and successfully engaged the target population. CONCLUSION: Mountain biking is a growing cause of serious injuries. Young males are principally at risk and serious injuries result from intended activity and despite protective equipment. Injury prevention programs were developed to address these concerns.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".