Association Between Injury History And Incidence Of Lower Extremity Injury During Canada Games Competition
Bibliographic record
Abstract
Injuries during elite level competition like Canada Games occur with injury history as one of the predictors of future injury. This association is unknown in Canada Games. PURPOSE: To determine the association between injury history and incidence of lower extremity joint injury during Canada Games METHODS: Data from 2009 - 2019 Canada Games (8710 male and 8391 female athletes) were de-identified by Canada Games Council for analysis. Injury data were categorized for previous injury and injury type and location. Injury history was self-reported and included concussion (CON), major surgical procedure (SUR), neck and back (N/B), trauma to joint and bone (J/B), and trauma to ligament and tendon (L/T). Injury from Canada Games competitions were categorized to include ankle, knee, hip, and patellofemoral joint (PF) injuries. Chi-Square (χ2) test of independence determined association and likelihood ratio (LR) between injury history and incidence of lower extremity joint injury. IBM SPSS (Version 26) was used for statistical analysis (p-value <0.05). RESULTS: 475 ankle, 503 knee, 253 hip, and 94 patellofemoral joint injuries were reported during 10 years of Canada Games. There were significant associations between history of N/B injuries with ankle injuries (χ2 = 5.793; p = .016; LR =5.509; p = .019), history of J/B with hip injuries (χ2 = 4.410; p = .036; LR =4.700; p = .030), history of N/B injuries with knee injuries (χ2 = 5.595; p = .018; LR =6.182; p = .013), and history of J/B with PF injuries (χ2 = 10.693; p = .001; LR =9.237; p = .002). There were significant associations between history of SUR and meniscus injuries (χ2 = 5.941; p = .015; LR =4.660; p = .031) and patellar femoral pain syndrome (χ2 = 3.933; p = .047; LR =3.242; p = .072), history of J/B and contusions (χ2 = 4.026; p = .045; LR =4.493; p = .034), tendinopathy (χ2 = 4.053; p = .044; LR =3.697; p = .054), and patellar femoral pain syndrome (χ2 = 9.585; p = .002; LR =8.215; p = .004), and history of L/T and sprains (χ2 = 4.366; p = .037; LR =4.287; p = .038). CONCLUSIONS: History of N/B, SUR, L/T, and J/B injuries were associated with lower extremity joint injury. Results support previous literature suggesting that injury history is related to future injury. Supported by: Brock University Match of Minds & Canada Games Grants; acknowledgment: Canada Games Council for providing de-identified data.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".