Influence of playing surface on match injury risk in men's professional rugby union in England (2013–2019)
Bibliographic record
Abstract
The use of artificial playing surfaces in professional rugby union is growing, but their effect on the injury risk profile remains unclear. The aim of this study was to examine the effect of playing surface on match injury risk in men's professional rugby in England. Six seasons of injury data (2013/14-2018/19) were collected from 15 professional English, men's rugby teams participating in domestic and European competition. The incidence, severity, and burden of match injuries were compared across playing surfaces. The dataset included 3351 injuries from a combined European and domestic data set (separated in to 2 categories; artificial and natural/hybrid surfaces) and 2675 injuries from a domestic only dataset (separated into 3 categories; artificial, hybrid, and natural surfaces). There were no differences in incidence rates between surface types on combined European and domestic match data, but injury burden was significantly greater on artificial (3082 days/1000 h, 95% CI 2847-3337) in comparison with natural/hybrid surfaces (2364 days/1000 h, 95% CI 2277-2454, p < 0.001). These differences were primarily driven by a significantly greater mean severity of hip/groin, and foot/toe injuries on artificial surfaces. This is the largest study to date to examine the relationship between surface type and injury risk in rugby union. The average severity and burden of injuries sustained on artificial surfaces was significantly greater compared with those sustained on hybrid/natural grass surfaces. This study can inform those involved in selection of surface for elite sport, weighing up the positive and negative elements of the varying surface types.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".