A Comparison of Suspected Injuries, Suspected Concussions, and Match Events in Male and Female Canadian High School 15s and 7s Rugby
Bibliographic record
Abstract
OBJECTIVE: To compare match event rates and suspected injury and concussion rates between 7-a-side (7s) and 15-a-side (15s) female and male high school rugby union players using video analysis (VA). DESIGN: Cross-sectional video analysis study. SETTING: Alberta high school rugby competitions. PARTICIPANTS: Senior high school rugby players (ages 14-18 years) in Calgary, Alberta, participating in the March to June 2022 season. ASSESSMENT OF RISK FACTORS: Video analysis of high school rugby matches in 7s and 15s. MAIN OUTCOME MEASURES: Univariate Poisson regression analyses were used to determine rates of match events, video-identified suspected injuries, and suspected concussions per 1000-player-hours. Incidence rates and incidence rate ratios (IRR) were used to compare between 7s and 15s (15s referent group) and females and males (male referent group). RESULTS: Suspected injury rates ranged from 115.0 to 223.6/1000 match hours, while suspected concussion rates ranged from 61.5 to 93.2/1000 hours. The male 7s cohort reported the highest suspected injury and suspected concussion rate, with no significant differences between male or female cohorts across formats. The tackle accounted for 84.6% of all injuries. Despite 30% more tackles in female compared with male 15s, tackle-related injury rates were similar between sexes [IRR = 1.1 (95% CIs: 0.7-1.6)]. CONCLUSIONS: This study did not find any differences in suspected injury or suspected concussion rates between sexes or formats of the game. The proportion of injuries recorded in the tackle and the high reported suspected injury rates does suggest the need for further investigation into tackle proficiency, injury prevention intervention evaluation, and potential 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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".