Injuries in Elite Men’s Rugby Union: An Updated (2012–2020) Meta-Analysis of 11,620 Match and Training Injuries
Bibliographic record
Abstract
BACKGROUND: The most recent meta-analytic review of injuries in elite senior men's Rugby Union was published in 2013. The demands of the game at the elite level are continually changing alongside law amendments and developments in player preparation. As such, an updated meta-analysis of injury data in this setting is necessary. OBJECTIVE: To meta-analyse time-loss injury data in elite senior men's Rugby Union between 2012 and 2020. METHODS: Electronic databases were searched using the keywords 'rugby' and 'inj*'. Nineteen studies met the inclusion criteria. Injury incidence rate data were modelled using a mixed-effects Poisson regression model. Days missed data were modelled using a general linear mixed model. RESULTS: The included data encompassed a total of 8819 match injuries and 2801 training injuries. The overall incidence rate of injuries in matches was 91 per 1000 h (95% confidence interval (CI) 77-106). The estimated mean days missed per match injury was 27 days (95% CI 23-32). The overall incidence rate of match concussions was 12 per 1000 h (95% CI 9-15). The overall incidence rate of training injuries was 2.8 per 1000 h (95% CI 1.9-4.0). Playing level was not a significant effect modifier for any outcome. CONCLUSIONS: The injury incidence rate and mean days missed per injury in the present meta-analysis were higher, but statistically equivalent to, the 2013 meta-analysis (81 per 1000 h and 20 days, respectively). The injury incidence rate for match injuries in elite senior men's Rugby Union is high in comparison to most team sports, though the training injury incidence rate compares favourably. The tackle event and concussion injuries should continue to be the focus of future preventative efforts.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".