Profile of an American Amateur Rugby Union Sevens Series
Bibliographic record
Abstract
BACKGROUND: Rugby union will enter the Olympic arena as Rugby Sevens in 2016. PURPOSE: To investigate the injury rate, injury type, and nature of injuries sustained in an amateur American rugby union sevens tournament series. STUDY DESIGN: Descriptive epidemiology study. METHODS: The rate, demographics, and characteristics of injury were evaluated in 1536 rugby union sevens players, from 128 sides, competing in 4 amateur 1-day tournaments in a USA Rugby local area rugby union. RESULTS: Forty-eight injuries occurred over 4 tournaments, for an injury rate of 55.4 injuries per 1000 playing hours. Head and neck injuries were most common (33.3% of injuries), followed by upper extremity (31.3%), trunk (18.8%), lower extremity (14.6%), and physiologic injuries (2.1%). The most common type of injury was ligament sprain (25.0%); followed by concussion (14.6%), hematoma/contusion (12.5%), muscle strain (10.4%), and abrasion (8.3%). Tackling was the most common mechanism of injury (74.5%). Males were injured at a significantly higher rate than females (RR, 7.5, P < .01), but no significant difference was observed based on player position (P = .08). CONCLUSION: Injuries are common among American amateur rugby athletes, with a substantial proportion involving the head and neck region. CLINICAL RELEVANCE: Understanding injury patterns in an American rugby union will be important for formulating future injury prevention, assessment, and treatment 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".