The Influence of Time of Season on Injury Rates and the Epidemiology of Canadian Football Injuries
Bibliographic record
Abstract
OBJECTIVE: To describe injury rates and injury patterns in the Canadian Football League (CFL) according to time during the season, player position, injury type, and injury location. DESIGN: Prospective, cohort study. SETTING AND PARTICIPANTS: Eight seasons from CFL injury surveillance database. INDEPENDENT VARIABLES: Depending on the analysis, time of season (preseason, regular, and playoffs), player position, injury type, and injury location. MAIN OUTCOME MEASURES: Medical attention and time-loss injury rates per 100 athletes at risk (AAR), and prevalence of time-loss injuries per week. RESULTS: The average game injury rate was 45.2/100 AAR medical attention injuries and 30.7/100 AAR time-loss injuries. Injury rates declined by 1% per week over the season for both medical attention (rate ratio = 0.99) and time-loss (rate ratio = 0.99) injuries, with a substantial decline during the playoffs compared with preseason (rate ratio = 0.70-0.77). The number of ongoing time-loss injuries increased over the course of the regular season. Quarterbacks, offensive backs, and linebackers had the highest game injury rates. Joint/ligament and muscle/tendon injuries were the most common injury types for games and practices, respectively. The lower extremity was the most commonly affected area, specifically the lower leg/ankle/foot and hip/groin/thigh. CONCLUSIONS: There was a 1% decline in injury rate per week during the season and a 30% decline during the playoffs. The number of ongoing time-loss injuries increased over the regular season. Current results can aid league officials and medical staff in making evidence-based decisions concerning player safety and health.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".