Epidemiology of Injuries in High School Football Players: A Prospective Cohort Study
Bibliographic record
Abstract
Objective: To estimate the incidence and severity of injuries sustained by a group of high-school football players and to identify risk factors associated with these injuries Design: Observational cohort study Settings: High school football programs in Quebec, Canada Participants: 707 male high-school football players were recruited and entered the study. They had to come from one of four participating high schools to be included in the study. All players completed the survey Interventions: Participants filled out a questionnaire about sociodemographic data, football experience, and life habits. They were observed throughout a football season and any injury was entered into a database by the team’s trainer Main outcome measures: Participants were divided into “injured” or “non-injured” at the end of the observation period. Each injury was analyzed independently. Incidence rates of injury were calculated per 1000 Athlete-Exposures (AEs) and potential risk factors were assessed Results: 294 players sustained 413 injuries (11.67 per 1000 AEs; 95% Confidence Interval (CI) 11.63-11.7). Injuries were more frequent in game than practice (Relative Risk (RR) 41.67, CI 30.5-56.9). The most frequent injuries were concussions (3.11 per 1000 AEs; CI 3.09-3.13). The presence of a previous injury was associated with significantly more subsequent injuries (p=0.0006). Other significant associations were the presence of another active injury, tobacco use, and a higher BMI (p<0.05) Conclusion: Injuries are frequent in high-school football players. Future prevention strategies should focus on athletes with a previous history of injury. Reducing concussion rate is also necessary in this growing 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".