603 FO07 – Understanding injury and concussion in youth female rugby: a ‘scrum’ble to catch up!
Bibliographic record
Abstract
Background Rugby union (hereafter rugby) is a high-intensity collision sport. Consequentially, the risk of injury and concussion is high. To date, youth research in rugby has focused almost exclusively on males, despite growing popularity of the female game. Objective To examine the rates of injury and concussion in youth female rugby in England. Design Prospective cohort study. Setting English youth female rugby. Participants 248 female adolescent (ages 14–18) rugby players participating in schools, community club or developmental player pathway rugby during the 2022–2023 season. Assessment of Risk Factors Players were observed for exposure to matches and training sessions over the course of one season (2022–2023). No interventions were implemented outside of normal rugby participation. Main Outcome Measures Injury (medical attention and/or time loss) rates (IR) and concussion rates (CR:/100 players/season). Results A total of 204 rugby-related injuries were reported, of which 148 were from matches, 50 were from training, and 6 from other circumstances. The match IR was 59.7/100 players/season (95% CIs: 50.5–70.1) and match CR was 17.7/100 players/season (95% CIs: 12.9–23.8). The most common injury locations were the head (n=45, 30%), knee (n=22, 15%) and ankle (n=21, 14%). The tackle was responsible for the highest proportion of injuries (60% tackle-related: 30% tackler, 30% ball carrier), followed by the ruck (11%). Training IR was 20.2/100 players/season (95% CIs: 15.0–26.6) and training CR was 3.6/100 players/season (95% CIs: 1.7–6.9). The ankle was the most injured location in training (n=13, 26%), followed by the head (n=9, 18%) and knee (8, 16%). Conclusions The head was the most injured location in matches and the tackle event was associated with the highest injury rate. Injury and concussion rates in female youth rugby players are high and prevention strategies are required to minimise this risk.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".