Injuries and Concussions in Female High School Rugby: Prevention is Worth a Try
Bibliographic record
Abstract
Objectives: To describe injury and concussion rates and mechanisms in female high school rugby players. Design: Two-year prospective cohort study. Setting: High school rugby. Participants: Participants included 214 female High school rugby players (year 1) and 207 female High school players (year 2) from the Calgary Senior High School Athletics Association 2018 and 2019 rugby competition. Intervention: None. Main Outcome Measures: Match and training injury and concussion. Injury definition included any injury resulting in time loss, inability to complete a session, and/or requiring medical attention. Details of reported injuries were collected on injury report forms and validated by a certified athletic therapist on a validated online injury surveillance platform. Exposure hours for players were tracked using paper or virtual weekly exposure forms by team designates. Results: A match incidence rate (IR) = 93.7 injuries/1000 match hours (95% confidence intervals (CI): 78.6-11.7) and training IR = 5.3 injuries/1000 training hours (95% CI: 4.0-6.9) were estimated. The tackle accounted for 109 (70%) match and 37 (44%) training injuries. Tackling was the most frequent mechanism of injury (IR = 37.5 injuries/1000 match hours, 95% CI: 27.5-51.8 and 1.2 injuries/1000 training hours, 95% CI: 0.7-2.4). Sixty-two match concussions (IR = 37.5 concussions/1000 match hours, 95% CI: 26.8-52.3) and 16 training concussions (IR = 1.0 concussions/1000 training hours, 95% CI: 0.7-1.4) occurred. Of 78 reported concussions, 78% for match and 56% for training were physician diagnosed. Tackling was the most frequent mechanism of concussion in matches (IR = 18.1 concussions/1000 match hours, 95% CI:11.4-28.6). Conclusions: Injury and concussion rates in female high school rugby are high. The tackle accounted for the highest proportion of injuries. Prevention strategies (eg, tackle policy change, tackle-training programs, and neuromuscular training) should be explored to increase sport safety.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".