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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".