Twenty year analysis of professional men’s rugby union knee injuries from the English premiership shows high rates and burden
Bibliographic record
Abstract
OBJECTIVES: To determine the rates, severity and burden of knee injuries in professional male rugby union from the English Premiership. METHODS: Injury and exposure data were captured over 20 seasons using a prospective cohort design. Knee injury incidence, days' absence and burden were recorded for each injury type and by pitch surface type for match and training. RESULTS: The rate of knee injury in matches was 9.8/1000 hours (95% CIs 9.3-10.3). Mean days lost were 50 (95% CI 46 to 53) in matches and 51 (95% CI 44 to 57) in training. In matches, medial collateral ligament injuries were the most common, while anterior cruciate ligament (ACL) injuries had the highest mean severity and burden. There was no significant change in the count of knee injuries over time; however, average severity increased significantly (annual change: 2.18 days (95% CI 1.60 to 2.77); p<0.001). The incidence of match knee injury was 44% higher on artificial pitches than grass pitches (incidence rate ratio: 1.44 (95% CI 1.21 to 1.69); p<0.01), with no significant difference in severity between surfaces. In matches, the tackle was the event most commonly associated with knee injuries for all diagnoses, except ACL injuries (running). In training, running was a more common injury event than the tackle. CONCLUSION: Knee injuries in matches are common and severe in English professional men's rugby union. Despite an increased focus on player conditioning and injury prevention throughout the study period, rates of knee injury remained stable, and resulting days' absence increased. New strategies for the prevention of knee injuries should be considered a priority.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".