Do Mountain Bikers Know When They Have Had a Concussion and, Do They Know to Stop Riding?
Bibliographic record
Abstract
OBJECTIVE: To establish the prevalence of concussions in mountain bikers and to determine factors that increase their risk of concussion. Secondary objectives include determination of whether mountain bikers have undiagnosed concussions, continue to ride after experiencing concussion symptoms, and if they knowingly ride with a broken helmet. DESIGN: Retrospective survey. SETTING: Seven-day mountain bike stage race. PARTICIPANTS: Two hundred nineteen mountain bikers. MAIN OUTCOME MEASURES: Number of rider concussions diagnosed, number of riders experiencing concussion symptoms without diagnosed concussions, number of riders who continue to ride after experiencing a concussion symptom, and number of riders who rode with a broken helmet. INDEPENDENT VARIABLES: The independent variables studied included age, gender, nationality, number of times riding in past year, style of riding (cross-country, downhill, or freeride), years mountain biking, years mountain bike racing, whether they are a sponsored cyclist, and whether they also ride a road bike. RESULTS: Fifteen of 219 mountain bikers (6.9%) had a diagnosed concussion after being hit in the head while mountain biking within the past year, with older riders having a decreased risk [odds ratio (OR), 0.91; P = 0.04], and sponsored riders having a 5-fold increased risk compared with nonsponsored riders (OR, 4.20; P = 0.05). Twenty-eight riders (12.8%) experienced a concussion symptom without being diagnosed with a concussion and 67.5% of the riders who experienced a concussion symptom continued to ride afterward. Overall, 29.2% of riders reported riding with a broken helmet. CONCLUSIONS: The yearly prevalence of diagnosed concussions in mountain bikers is 6.9%. More than one-third of mountain bikers do not recognize when they have had a concussion and continue riding after experiencing concussion symptoms or with a broken helmet. These behaviors increase their risk of worsening concussion symptoms and acquiring a second injury.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".