Injury incidence, characteristics and timing in amateur male rugby union: A prospective cohort study
Bibliographic record
Abstract
Rugby union has a high incidence of match injuries. However, there is limited information regarding the incidence and characteristics of match injuries in male amateur players. In particular, there is a lack of information regarding injury rates per match quarter. Investigating this may inform injury prevention strategies. The aim is to determine whether the rate and characteristics of injury vary with match quarter in male amateur rugby union players, regardless of whether the injury resulted in time loss from play. This prospective cohort study recorded and examined the number and characteristics of injuries during match quarters across a season of amateur rugby union. Team match exposure was recorded. Injuries were recorded by a team physiotherapist consistent with Rugby Injury Consensus Group guidelines. Matches were divided into quarters for data analysis, and statistical significance was determined using Chi-square analysis. 127 players sustained 207 injuries across 18 games. The injury incidence was 164 injuries/1000 match hours. There was a significant (p<0.001) difference in the number of injuries between match quarters, with the greatest number in the fourth, followed by the second, third, and first quarter. Forwards had a statistically significant higher rate of injury between quarters. Injury incidence in amateur rugby is higher than previously reported. Injury rates in amateur male rugby increase at the end of each match half, peaking in the fourth quarter. These findings contribute to the understanding of the aetiology of injury in amateur rugby union.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".