The Distribution of Injuries in Men's Canada West University Football: A 5-year Analysis
Bibliographic record
Abstract
We conducted a prospective cohort study from 1993 to 1997 to determine the frequency and severity of injury in men's Canada West university football. The Canadian Intercollegiate Sport Injury Registry was used to document baseline preseason data, daily athlete participation, and subsequent injury from five varsity football teams. An injury was defined as "any injury resulting in one or more complete or partial sessions of time loss" or "any concussion or transient neck neurologic injury." The annual proportion of injured athletes ranged from 53.5% to 60.4%, with a 5-year total of 1,811 injuries. Regression analysis indicated that the rate of nonconcussion, nonneck neurologic injuries increased. Concussion (N = 110), hamstring strain (N = 88), and brachial plexus (N = 84) injuries were the most common, specific injury diagnoses. Knee injuries resulted in the highest rate of severe (greater than or equal to 7 sessions of time loss) injury and resulted in the most time loss (3,350.5 sessions). Ligament sprains and muscle strains and spasms accounted for approximately half of all injury diagnoses. A total of 1,173 injuries (65%) were related to contact between players or between players and other obstacles. Future studies should be conducted to identify risk factors for the ultimate purpose of implementing injury prevention strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".