The Effect of Bye Weeks on Injury Event Rates in the Canadian Football League
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of bye weeks (no practices or games) on the injury event rate in the Canadian Football League (CFL). DESIGN: Historical (retrospective) cohort study. SETTING: CFL. PARTICIPANTS: CFL athletes between 2011 and 2018. INTERVENTION: CFL pseudorandom assignment of bye weeks each season (2011-2013: 1; 2014-2017: 2; 2018: 3). MAIN OUTCOME MEASURES: Game injury incident rate ratio (IRR) in the week following a bye week compared with non-bye weeks. Sensitivity analyses: IRR for the 2 and 3 weeks following a bye week. We conducted exploratory analyses for combined game and practice injury events because we did not have the number of players exposed during practice. RESULTS: The IRR was 0.96 (0.87-1.05), suggesting no meaningful effect of a bye week on the post-bye week game injury event rate. We obtained similar results for cumulative game injury events for subsequent weeks: IRR was 1.02 (0.95-1.10) for the 2 weeks following the bye week and 1.00 (0.93-1.06) for the 3 weeks following the bye week. The results were similar with 1, 2, or 3 bye weeks. However, the combined game and practice injury event rate was increased following the bye week [IRR = 1.14 (1.05-1.23)]. These results are expected if the break period results in medical clearance for preexisting injuries; increasing pain in these locations following the bye week would now be considered new injuries instead of "exacerbations." CONCLUSIONS: Bye weeks do not appear to meaningfully reduce the injury event rate. Furthermore, there was no injury reduction when adding additional bye weeks to the schedule.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".