208 What about BMX? A scoping review of injuries, risk factors, and prevention strategies
Bibliographic record
Abstract
Background Bicycle motocross (BMX) was officially added to the Olympics in 2008. Participation has increased over the last decade and is listed as a top sport for injury rates in multisport studies. Before effective prevention programs can be designed and implemented, it is important to understand injury risk, risk factors and potential prevention strategies. Objective To examine the evidence on injury incidence, prevalence, risk factors, prevention strategies, and prevention implementation in BMX. Methods Five electronic databases were systematically searched in July 2020 for studies that included BMX injury as the main topic or subtopic. Two reviewers screened all studies and extracted data independently. Conflicts were resolved via consensus and a third reviewer. Results Of the 1615 unique articles screened, 36 met the inclusion criteria. Most injury surveillance based studies were conducted at elite competitions (e.g. BMX Cycling European Championship, Olympic Games, UCI BMX World Championship) or using data from the emergency department. The most common BMX injuries were fractures, lacerations, abrasions, and contusions. Risk factors included age, sex, number of riders per race, history of injury, and bicycle characteristics. Prevention strategies are limited and have not been appropriately evaluated; one study found that wearing a neck brace may reduce the number and magnitude of rotational accelerations at the head during BMX racing, but this was not evaluated for its effect on injury rates. Conclusions Most BMX studies focus on injury characteristics and do not use appropriate injury surveillance methodology. Studies based on emergency room data may underestimate less severe injuries and do not provide adequate measures of sport exposure. Reducing the number of riders per race may be a promising modifiable risk factor that requires further examination. More rigorous community-based prospective studies examining injury rates, 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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".