The influence of match exposure on injury risk in elite men's rugby union
Bibliographic record
Abstract
OBJECTIVES: To investigate the influence of previous season match exposure on injury incidence and burden in elite men's rugby union. DESIGN: A three-season (2016-17 to 2018-19) retrospective cohort design was used to collect and analyse injury and exposure data across English Premiership rugby union teams. METHODS: Generalised linear mixed-effects models were used to model the influence of match exposure (all match involvements, match involvements of ≥20 mins, and full-game equivalents) upon match and training injury incidence and burden in the following season. RESULTS: Involvement in ≥31 matches within a season was associated with substantially increased match and training injury burden in the following season. Match exposure was not clearly associated with injury incidence in the following season. The increased match injury burden associated with higher match involvements appeared to be driven by an increased risk for older (>26 y) Forwards, whilst the increased training injury burden associated with higher match involvements appeared to be driven by an increased risk for older (>26 y) Backs. CONCLUSIONS: The present study demonstrates that all match involvements, regardless of duration, should be considered when exploring associations between match exposure and injury risk. High match involvements (≥ 31 matches) are associated with elevated injury burden, in both matches and training, in the following season. The physical and psychological load of players with high previous-season match exposure should be carefully managed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".