Injuries during the 2006 Women’s Rugby World Cup
Bibliographic record
Abstract
BACKGROUND: Although there have been reports of injury rates in men's World Cup rugby, there has been no research into injury rates in women's international rugby. OBJECTIVE: To determine the rate of injury at the 2006 Women's Rugby World Cup (WRWC). METHODS: All participating teams in the 2006 WRWC were enrolled prospectively in this study. Healthcare workers for each team collected data on training and game exposure and information on injuries. RESULTS: 55 players (16%) sustained at least one injury (10.0/1000 player hours); 4 players (1%) sustained two injuries. 45 injuries occurred during games (37.5/1000 player hours) and 14 injuries occurred during practice (12.5/1000 player hours), a statistically significant difference (RR = 12.5, 95% CI 6.9 to 22.8). The injury rate was 39.3/1000 player hours for the forwards and 42.2/1000 player hours for the backs (RR = 1.3, 95% CI 0.7 to 2.3). The front row had the highest injury rate (62.5/1000 player hours). 63.6% of injuries occurred during the tackle. Most injuries occurred to the neck, knee and head/face. The majority of injuries were sprains, muscle injuries and contusions. There were five fractures during the event and four reported concussions. CONCLUSION: Female players sustained a considerable rate of injury during the 2006 WRWC. Backs had a slightly higher rate of injury than forwards, but the players most often injured were in the front row. The neck/cervical spine and knee were the most commonly injured regions, followed by the head and face. This is the first study to examine injury rates in female rugby players at a World Cup and reports valuable data in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".