Mountain bike terrain park injuries: an emerging cause of morbidity
Bibliographic record
Abstract
Background The popularity of mountain biking (MB) has led to the development of commercial MB parks. Little is known about the injury profile and risk factors in these areas. Objective To determine the injury profile and risk factors for severe injury among cyclists in MB parks. Design Prospective case-control study. Cases were hospitalised cyclists injured in MB parks. Controls were cyclists injured in MB parks seen and discharged from the emergency department (ED). Setting Four EDs in Calgary, Alberta, Canada. Participants Recreational cyclists injured in a MB park who presented to one of the study EDs from May 2008 to August 2010. 351 patients were interviewed. Assessment of risk factors Crash circumstances were captured through interviews and injury data through medical chart review. Main outcome measurements Severe injury as defined by hospitalisation; levels within factors were compared using OR and 95% CI. Results 23 participants were hospitalised (cases).The most common body region injured was the head/neck/face among cases, and the upper extremities among controls. 21% of cases and 9% of controls were female. A greater proportion of cases than controls were older than 25 years (22% vs 15%, respectively). Full-face helmets were used less among cases than controls (21% vs 41%, respectively). Arm and elbow protection was used more among cases than controls (arm: 13% vs 2%; elbow: 22% vs 8%). On univariate analyses, we found an indication of increased odds of severe injury among females (OR=2.8; 95% CI 0.96 to 8.06). Riding a new bicycle (OR=2.74; 95% CI 1.16 to 6.45) and cycling on grass compared with dirt (OR=7.06; 95% CI 1.21 to 41.33) increased the odds of severe injury. Conclusion Preliminary analysis suggests surface and experience-related characteristics may increase the risk of severe injury. Case-control differences were noted in protective equipment use.
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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 | 0.002 |
| 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".