It Takes Two to Tango: High Rates of Injury and Concussion in Ball Carriers and Tacklers in High School Boys' Rugby
Bibliographic record
Abstract
OBJECTIVE: To examine injury and concussion rates, mechanisms, locations, and types of injury in Canadian high school male rugby. DESIGN: Prospective cohort study. SETTING: High school male rugby. PARTICIPANTS: A total of 429 high school players (2018: n = 225, 2019: n = 256) were recruited from 12 teams in 7 schools in Calgary, Canada. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Injury surveillance included baseline questionnaires, weekly exposure, and injury reports. Injuries included those requiring medical attention, resulted in time loss and/or inability to complete a session. Concussion was defined as per the fifth Consensus on Concussion in Sport, and all players with a suspected concussion were referred to a study sport medicine physician. RESULTS: A total of 134 injuries were captured, leading to an injury incidence rate (IR) of 57.9/1000 hours [95% confidence intervals (CIs): 45.4-73.8]. Median time loss was 6 days (range: 0-90). Injuries to the head were the most common (40%), followed by shoulder (12%) and ankle (10%). The concussion IR was 22.0/1000 hours (95% CIs: 15.9-30.4), which was the most common injury type (38%), followed by sprain (20%) and strain (15%). Sixty-five percent of injuries occurred in the tackle (ball carrier 35%, tackler 30%) and 76% of concussions (ball carrier 41%, tackler 35%). CONCLUSIONS: The rate of injury and concussion in Canadian youth high school male rugby is high, with tackle-related injuries and concussions the most common. Given this, there is a critical need for implementation of prevention strategies, in particular targeting concussion and the tackle event (eg, neuromuscular, tackle training, and law changes).
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".