Hurt on the Hill: A Longitudinal Analysis of Obstacle Course Racing Injuries
Bibliographic record
Abstract
BACKGROUND: Obstacle course racing (OCR) has become a popular sport in recent years as it challenges participants' mental and physical endurance through a combination of trail running and obstacles. There is currently only a limited amount of published work reporting injury types or rates at these events. PURPOSE: This study aims to build on the current literature, analyzing injury rates and patterns at OCR events. METHODS: A secondary data analysis of deidentified medical charts from 33 OCR events in Canada from 2015 to 2017 was conducted. The scope of on-site care was first aid. STUDY DESIGN: Descriptive epidemiology study. RESULTS: A total of 1782 injuries occurred over 3 seasons from 73,366 participants, with an overall average injury rate of 2.4%; 1.0% (n = 17) of injuries required emergency medical services transport to a local emergency department, and the majority of these injuries were musculoskeletal in nature. The most common injuries treated were lacerations and musculoskeletal-related injuries; 93.9% of the injuries were able to be treated on site. These findings, in conjunction with the published literature, suggest that OCR medical teams should anticipate injury rates of up to 5.0% and a transportation rate of approximately 4.5% of those injuries to local emergency departments. CONCLUSION: The injury and transportation rates found in this study are congruent with the current literature and, most notably, they demonstrated a stable trend across a variety of course lengths (5-42 km ) and numbers of obstacles (≥20). While the majority of injuries may be treated on site, there is still a serious potential for life-threatening emergencies to occur.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".