451 Who? What? Where? Why? Describing the patterns of injury in high school male collision sports
Bibliographic record
Abstract
Background Collision sports (Canadian football, ice hockey, lacrosse, rugby) are popular among Canadian male youth, however it is recognised that collision sports are associated with a high risk of injury. Objective To describe the patterns of collision sport-related injury in Canadian male high school athletes. Design Secondary analysis of a cross-sectional survey. Setting High schools (Alberta, Canada) Participants 360 male students (of 2029 respondents), who play at least one of Football, Hockey, Lacrosse or Rugby. Assessment of Risk Factors An anonymous online survey included questions regarding the mechanism, site, type, and nature of collision sport injuries. Main Outcome Measurements Sport-related injury self-reported in the past year. Results Of the 2029 survey respondents, 958 (47.2%) were male of which 360 (37.6%) reported playing at least one collision sport. Of all serious injuries reported by males, collision sports accounted for 33% [hockey: 63(17%), football: 41(11%), lacrosse: 9(3%), rugby 8(2%)]. The head/face accounted for the largest proportion of injuries (hockey: 25.4%, football: 24.4%, lacrosse: 33.3%, rugby 50.0%). Concussion was the most common injury in rugby (50.0%) and football (24.4%) and fractures the most common in hockey (27.0%) and lacrosse (44.4%). Contact with another player was the most frequently reported mechanism of injury (rugby: 87.5%, football: 77.1%, lacrosse: 66.7%, hockey: 57.4%), with most injuries related to contact by a player who was bigger or the same size as the injured player (hockey/rugby:100%, lacrosse: 83.3%, football: 81.5%). Conclusions Sport-related injuries in male collision sports are common, with four sports accounting for 33% of all reported injuries across male Canadian high school sports. Head/face injuries were the most common, with the majority of injuries occurring due to contact with another player. There is scope to consider primary prevention strategies such as contact training and rule changes to address the risk of injury in youth collision sport.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".