The downhill race for a Rainbow jersey. The Epidemiology of Injuries in Downhill Mountain Biking at the 2023 UCI Cycling World Championships using the International Olympic Committee Consensus: A Prospective Cohort Study
Bibliographic record
Abstract
Abstract Introduction Downhill Mountain Biking (DHMTB) is one of the more spectacular sub-disciplines of mountain bike (MTB) cycling. The primary aim of our study was to prospectively document the injury rate, severity, aetiology, location and type during official training and racing by elite DHMTB riders during the 2023 UCI Cycling World Championships. Methods The participants of this prospective, observational study were elite male and female cyclists competing at the UCI DHMTB World Championships located in the Nevis range in Fort William, Scotland, in 2023. This study followed the injury reporting guidelines established by the International Olympic Committee (IOC), which include the STROBE-SIIS and the cycling-specific extension. Results Throughout the championships, 10.4% of riders sustained one injury, with 4.3% of riders injuring more than one location per injury event. The overall injury incidence was 3.3 injuries per 100 rides. The incidence rates were higher in the training group (6.4/100rides) than in the race group (2.3/100rides). There was a greater incidence of injury in females in the training 5.7/100 rides and racing 4.4/100rides compared to male riders. Female athletes experienced more severe injuries, with double the estimated time lost to injury. Additionally, female athletes were found to have a significantly greater risk of head injuries and concussions than males. Conclusion Overall, injuries are more prevalent in training than in competition. Compared with male DHMTB athletes, female DHMTB athletes are more at risk of injury and show a greater incidence of injury within official training and competition as well as more severe injuries. Summary Box What is already known Downhill Mountain Biking (DHMTB) is one of the more spectacular subdisciplines of mountain bike cycling and has been shown to have high injury prevalence. There is a lack of methodological homogeneity amongst the prospective injury surveillance studies conducted within DHMTB and across competitive cycling. No Study has currently reported injury incidence within elite DHMTB as per the International Olympic Committee (IOC) cycling extension recommendations. What this study adds Within DHMTB injury incident rates were higher in training (6.4/100rides) compared to racing (2.3/100rides). Overall Injury incident rate was significantly higher in females (5.1/100rides) compared to males (2.3/100rides). Female athletes have a 2.89 higher risk of Injury compared to Male DHMTB athletes. Female athletes have significantly higher risk of head/neck (RR 9.5) injuries and concussion (RR 6.34) compared to their male counterparts. How this study might affect research, practice, or policy The IOC Cycling Extension should acknowledge that when reporting injuries per 100 rides, the number of rides completed prior to injury should be collected to improve reporting accuracy. Female athletes may benefit from an extra official training ride to ease pressures during course familiarisation and reduce racing injury incidence. Female athletes may benefit from neck strengthening and resistance training to reduce the number of head and neck injuries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".