An American Experience with a New Olympic Collision Sport: Rugby Sevens
Bibliographic record
Abstract
Objectives: Rugby Sevens is a future Olympic collision sport that is played globally with a high incidence of injury. The sport is growing exponentially in the U.S. There is limited injury data on Rugby Sevens. Currently, there is no study evaluating incidence of injury in the National Rugby Sevens population in the U.S. The objective of this study was to characterize the injury rates among amateur Rugby Sevens players in the U.S. Methods: This was a prospective descriptive injury epidemiology study involving American Rugby Sevens tournaments from 2010-2013. The injury data were collected from both male and female Rugby Sevens players (13, 524 players) and conformed to the international consensus statement on rugby injury definitions. The study included a total of 1,127 teams competing in under-15 to national candidate level tournaments (USA Rugby Local Area, Territorial Union, National and All-Star Sevens Series, USA Sevens Invitational and Collegiate Rugby Championships). A total of 2746 matches were played, 2734 lasting 14-minutes (0.23 hours) in length and 12 lasting 20-minutes (0.33 hours) in length. The overall injury exposure for all players was 8858.9 playing hours (7 players per side * 2 teams per match * 0.23 hours per match * 2734 matches + 7 players per side * 2 teams per match * 0.33 hours per match * 12 matches). Player injury data were reported as proportion (%), mean (SD), and rate of injury as injuries per 1000 playing hours. Results: Incidence of combined medical attention and time-loss injuries was 97.7/1000 playing hours (n=875 injuries) (23.6±5 years old). Time-loss injuries alone were encountered at 43.1/1000 playing hours (n=380 injuries) (forwards, 14.3/1000, n=127; backs 25.7/1000, n=228) (RR: 1.8; 1.53-2.11, P < 0.001). Elite/national male competitors (242.6/1000) were injured more often than lower playing levels (147.6/1000) (P < 0.001). Most injuries were acute injuries (96%) that occurred during the tackling phase of play (97%), and it resulted mainly from contact with an opposing player (67%). The main injuries seen were ligament sprain in lower extremities (43%). Upper extremity injuries were seen more often among male players (76%) than female players (24%) (RR: 0.31, CI: 0.25-0.40, P < 0.001). Knee injuries were seen more often in women’s elite players than men’s elite players (P = 0.014). Head/neck injuries (backs, 58%; forwards, 42%) occurred more often in male players (74%) (RR: 0.34; CI: 0.26-0.44, P < 0.001). Conclusion: Injury prevention in American Rugby Sevens should focus on proper tackling technique as most injuries in our series occurred during tackling. We also saw a significant number of head/neck injuries in our U.S. playing population, which may reinforce the importance of proper tackling technique in this collision sport. The rate of head/neck injuries (23%) in our U.S. cohort (National candidates, 25%; lower competitors, 23%) was higher than the rate reported among international male Rugby Sevens players (5%) (Table 1). Overall, our National candidates had higher rates of time loss injuries than lower American amateur playing levels. Our observed injury rate among U.S. elite players is also higher than that reported for international male Rugby Sevens population. Understanding the injury profile of American Rugby Sevens is important to healthcare providers and would direct the growth and safety of this growing collision sport, allowing safe return-to-play decisions and formulation of prevention protocols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".