This is Sparta - A Five-Year Obstacle Course Racing Injury Analysis
Bibliographic record
Abstract
Introduction: Obstacle Course Races (OCR) are mass participation sporting events, challenging participants to complete physical and mental tasks over a variety of distances and terrains. The case series studied, Spartan Race, has races occurring in urban, rural, and wilderness venues, ranging from 5 to 42 kilometers, while incorporating 20 to 60 obstacles. Aim: To understand the injury rates, injury and illness patterns, and transport considerations within OCRs. Methods: A secondary data analysis of de-identified medical charts from 56 Spartan Race events occurring in Eastern Canada from 2014 to 2018 was performed. The scope of practice was first aid from 2014 to 2017, with the addition of advanced life support onsite in 2018. Results: Over 5 years, 2,387 injuries occurred among 127,481 participants, creating a patient presentation rate of 18.7/1000. Although the majority of injuries (92%; n=2,204) were treated onsite, a transport to hospital rate of 1.2/1000 (n=154) occurred along with an ambulance transport rate of 0.23/1000 (n=29). Lacerations (55%) and musculoskeletal (36%) injuries were the most frequent clinical presentations observed, whereas life-threatening emergencies (affecting airway, breathing, and circulation) were infrequent (n=10). Transport to the closest local tertiary care center was on average 49.8 kilometers (25.3 kilometers) and 40.5 minutes (17.9 minutes) away from the venue. Discussion: These results suggest that there may be an upper limit to the injury rates within Spartan Races. The majority of patient presentations were able to be treated onsite, supporting the need for a qualified onsite medical team to mitigate the strain on local healthcare systems. Although life-threatening emergencies were uncommon, they do occur, and medical teams must be appropriately prepared. Further research is needed to understand the staffing and equipment requirements of medical teams, the demographic information of the injured, and the examination of the impact OCR events have on the local health care systems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".