850 BO05 – Friday nights under northern lights: injury rates, burden, and risk factors in high-school aged tackle football players in Canada
Bibliographic record
Abstract
Background The burden of injury in tackle football is high and the growing popularity of the sport in Canada warrants research into the extent of the problem. Objective To investigate injury rates, types, burden, mechanisms, and risk factors in Canadian youth football. Design a cohort sub-study within the SHRed Concussions study. Setting & Participants Tackle football participants (n=599 males;ages) in Calgary during a nine-week Spring Football season. Identifiable Risk Factors Preseason baseline questionnaires and individual player-exposure hours (games and practices) were collected. Risk factors included; age, previous playing experience, 12-month history of injury, history of concussion, height, weight, BMI, and football-specific position-played. Risk factors were assessed using univariable Poisson regression analyses (offset for player-hours). Main outcome measures A validated injury surveillance methodology was implemented. Injuries were defined as, removal from play, missing a session, or requiring medical attention. Results The overall injury rate (IR) was 4.61 injuries/1000 player-hours (95%CI;3.84–5.53) or 29.4 injuries/100 player-seasons (95%CI;25.76–33.21). The game-IR (12.73/1000 game-playing-hours; 95%CI;9.81–16.52) was higher than practice-IR (2.22/1000 game-playing-hours; 95%CI;1.75–2.83) (IRR=5.79; 95% CI: 4.23–7.99). Overall time-loss IR was 3.43 injuries/1000 player-hours (95%CI: 2.84–4.14) and overall medical attention IR was 2.71 injuries/1000 player-hours(95%CI: 2.20–3.34). The game-concussion rate was 3.86/1000 game-hours (95%CI;2.66–5.42) and for practices, CR was 0.45/1000 practice-hours (95%CI: 0.25–0.75). The most common injury locations included: the head (28.4%), ankle (13.1%), wrist/hand (10.3%), knee (10.2%), and shoulder (8.5%). The most common injury types were ligament sprain (28.9%) and concussion (26.1%). Player-to-player contact was the most common mechanism of injury. The median time-loss following injury was 12.5 days (25th-75th%ile;5–22 days). Risk factors for injury included previous one-year history of injury, lifetime history of concussion, and morphological characteristics (height, BMI). Conclusions Adolescent Canadian tackle football has a high rate of injury and concussion. Future research should focus on prevention strategies with a strong focus on concussion prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".