Obstacle course runs: review of acquired injuries and illnesses at a series of Canadian events (RACE)
Bibliographic record
Abstract
BACKGROUND: The growing popularity of obstacle course runs (OCRs) has led to significant concerns regarding their safety. The influx of injuries and illnesses in rural areas where OCRs are often held can impose a large burden on emergency medical services (EMS) and local EDs. Literature concerning the safety of these events is minimal and mostly consists of media reports. We sought to characterise the injury and illness profile of OCRs and the level of medical care required. METHODS: This study analysed OCR events occurring in eight locations across Canada from May to August 2015 (total 45 285 participants). Data were extracted from event medical charts of patients presenting to the onsite medical team, including injury or illness type, onsite treatment and disposition. RESULTS: There were 557 race participants treated at eight OCR events (1.2% of all participants). There were 609 medical complaints in total. Three quarters of injuries were musculoskeletal in nature. Eighty-nine per cent returned to the event with no need for further medical care. The majority of treatments were completed with first aid and basic medical equipment. Eleven patients (2% of patients) required transfer to hospital by EMS for presentations including fracture, dislocation, head injury, chest pain, fall from height, and abdominal pain. CONCLUSIONS: We found that 1.2% of race participants presented to onsite medical services. The majority of complaints were minor and musculoskeletal in nature. Only 2% of those treated were transferred to hospital through EMS. This is consistent with other types of mass gathering events.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".