Injuries, Risk Factors, and Prevention Strategies in Bicycle Motocross (BMX): A Scoping Review
Bibliographic record
Abstract
CONTEXT: Bicycle motocross (BMX) has become increasingly popular since its inclusion in the 2008 Olympics, but it has some of the highest injury rates (IRs) in multisport studies. To support planning for tailored primary prevention, understanding gaps in BMX injury prevention is crucial. OBJECTIVE: To examine the evidence on injury incidence, prevalence, risk factors, prevention strategies, and prevention implementation in BMX. DATA SOURCES: Ovid MEDLINE, Embase, APA PsycInfo, CINAHL, and SPORTDiscus were searched systematically in June 2023. STUDY SELECTION: Articles including BMX and any injury as the main topic or subtopic were searched across multiple databases. STUDY DESIGN: A scoping review was designed following the PRISMA Extension for Scoping Reviews (PRISMA-ScR). LEVEL OF EVIDENCE: Level 4. DATA EXTRACTION: BMX injury incidences, prevalence, risk factors, prevention strategies, and prevention implementation were extracted. Two reviewers screened all studies and extracted data independently. RESULTS: Of the 1856 articles screened, 37 met inclusion criteria. Most studies used injury surveillance at elite competitions or emergency departments, and common injuries were contusions, lacerations, and fractures. IRs provided were based primarily on elite competition and were heterogeneous (eg, 2016 Olympics: 37.5 per 100 athletes; 2007 BMX World Championship: 11.7 per 100 athletes; 1989 BMX Euro Championship: 6.6 per 100 athletes). Only 1 study stratified IRs by BMX discipline (BMX freestyle: IR, 22.2 injuries per 100 athletes; BMX racing: IR, 27.1 per 100 athletes). Few prevention strategies have been evaluated, but reducing the number of riders per race could be helpful. CONCLUSION: Most BMX studies do not use recommended injury surveillance methodology. Studies based on emergency department data may underestimate minor injuries and do not adequately measure BMX exposures. Rigorous community-based prospective studies examining IRs for both BMX racing and freestyle, risk factors, and prevention strategies are needed to inform widespread evidence-based prevention strategies.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".