289 Injury rates and mechanisms of injury in female high school rugby
Bibliographic record
Abstract
Background In Canada, unlike many countries, youth rugby players often have their first exposure to the sport in high school (ages 15–16). There are few studies examining injuries in female high school rugby union. Objective To describe injury rates and mechanisms among females participating in high school rugby union. Design Prospective cohort study. Setting Rugby pitches (Calgary, Canada). Participants Female high school rugby players (ages 15–18) participating in 2018 (7 teams, n=214) and 2019 (7 teams, n=207) seasons. Assessment of Risk Factors Mechanism of injury was recorded by team designates on an electronic injury report form, validated by a certified athletic therapist. Main Outcome Measurements Training and match injuries were identified by a team designate or study therapist if the player 1) required medical attention, 2) was unable to complete the session, and/or 3) unable to participate in activity for ≥ one day. Results There were 155 match [93.7 injuries/1000-match-hours (95%CI, 78.6–111.7)] and 85 training injuries [5.3 injuries/1000-training-hours ( 95%CI, 4.0–6.9)] across two years of injury surveillance. Match injuries most commonly occurred while tackling [62 injuries (40%) 37.5 injuries/1000-match-hours (95%CI, 27.1–51.8), being tackled [47 injuries (30%), 28.4 injuries/1000-match-hours (95%CI, 20.3–39.8)], and during a ruck/maul [12 injuries (8%), 7.3 injuries/1000-match-hours]. Training injuries most commonly occurred while tackling [20 injuries (24%), 1.2 injuries/1000-training-hours (95%CI, 0.7–2,4)], being tackled [17 injuries (20%), 1.1 injuries/1000-training-hours (95%CI, 0.7–1.7)], and running [9 injuries (11%), 0.6 injuries/1000-training-hours (95%CI, 0.3–1.0)]. Conclusions Tackling was identified as the most common mechanism of injury among female high school rugby players, with the highest rates in the active tackler during matches. Safe tackling interventions are an ideal primary prevention target to reduce the risk of injury within this population.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".