778 FO12 – From scrum to sling: the shoulder injury landscape in 19 seasons of professional rugby union
Bibliographic record
Abstract
Background Professional rugby union is a fast-paced collision sport with a high rate of injury. With the tackle often being a direct and forceful contact between opposing players at the shoulder level, it is a typical injury location. Objective To describe shoulder match and training injury rates, diagnosis, severity, and mechanisms in professional men’s rugby over 19 seasons. Design Prospective cohort study. Setting Professional Rugby Union (2002/2003 – 2021/2022). Participants Elite male athletes participating in a professional rugby league. Data were collected from all participating teams during each season of play (12/13 teams per season for 19 seasons). Interventions/Risk Factors Match and training exposure, as well as injuries were collected by club staff. Main Outcome Measures Shoulder-related injuries included any clavicular, scapular, acromioclavicular joint, rotator cuff, or shoulder joint, and surrounding tissue impairment. Any injury resulting in the athlete being unable to participate in rugby for >24 hours after the day of injury were included. Results A match shoulder injury rate of 9.01 per 1000 match-hours (n=1330) was observed. The training shoulder injury rate was 0.15 per 1000 training hours (n=362). Shoulder injury diagnoses most frequently involved the acromioclavicular joint (n=595, 35%), dislocations and instability (n=432, 27%), and impingement/rotator cuff injury (318, 19%). The primary injury pathologies based on orchard code were ligament injuries (n=675, 39%), cartilage injuries (n=149, 9%), and muscle contusions (n=143, 8%). Time-loss due to shoulder injury was a median of 15 days (IQR 5–43 days). The mechanism of most shoulder injuries was tackling (n=592, 35%) or being tackled (n=419, 25%). Conclusions Shoulder injuries resulted in substantial time-loss from sport. The injury rates were similar to what has been previously reported. With such little change in match and training injury rates observed, novel training strategies and policies to prevent shoulder injury merits further investigation and innovation avenues.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".