Is There an Association Between Injury History and Lower Extremity Joint Injury During Canada Games Competition?
Bibliographic record
Abstract
Background Injuries during elite level competition like the Canada Games, occur frequently and injury history is one of the strongest predictors of future injury; however, this association is unknown in the Canada Games. Purpose To determine the association between injury history and incidence of lower extremity joint injury during Canada Games competition. Methods Data from the 2009 – 2019 Canada Games (8710 male and 8391 female athletes) competitions were de-identified by the Canada Games Council for analysis. Injury data were cleaned and categorized for previous injury and injury type and location. Injury history was self-reported and included concussion, major surgical procedure, neck and back, trauma to joint or bone, and trauma to ligament or tendon. Injury from the Canada Games competitions were categorized to include ankle, knee, hip, and patellofemoral joint injuries. Chi-Square (χ 2 ) test of independence determined association between injury history and incidence of lower extremity joint injury during Canada Games competition. IBM SPSS (Version 26) was used for statistical analysis ( p -value < 0.05). Results Four hundred and seventy-five ankle, 503 knee, 253 hip, and 106 patellofemoral joint injuries were reported during 10 years of Canada Games competitions. There were significant associations between history of neck and back injuries with ankle injuries and knee injuries, history of trauma and overuse of ligament or tendon with hip injuries and history of trauma or overuse of joint or bone with patellofemoral joint injuries. Conclusion These findings support previous literature suggesting that injury history is associated with future injury. Level of Evidence 3
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".