Evaluation of injury surveillance data collected during major multisport games: Informing a standardized prospective injury surveillance system for the Canada Games
Bibliographic record
Abstract
This thesis includes two projects focused on the injury surveillance system used by the Canada Games (CG). The first, examined the interrater reliability of coding of injury information collected using the injury surveillance system employed at the CG. The injury report form data from the 2017 and 2019 CG was independently coded by two researchers with sports medicine backgrounds and based on the categorization and definitions used by the 2020 International Olympic Committee (IOC) consensus statement on methods for recording and reporting injury and illness data and the Orchard Sports Injury and Illness Classification System (OSIICS). The level of agreement based on percent agreement and Cohen’s kappa analysis (most interrater reliability was between moderate and substantial) was then determined between the two coded sets of data. The average percent agreement between the two researchers was 78.97% and the available information resulted in 8% of unknown or undiagnosed interpretations. The second project investigated injuries in both male and female able-bodied athletes competing in the same sports at the CG. Available registration and medical reports from the 2009-2019 summer and winter games were coded using the IOC consensus statement and OSIICS. The Summer Games had an incidence rate of 13.57 injuries (95%CI 12.70-14.48) and 13.22 injuries (95%CI 12.39-14.10) per 1000 athlete-days for female and male athletes, respectively, with an incidence rate ratio of 1.02 (95% CI 0.93-1.12). The winter Games had an incidence rate of 13.68 injuries (95%CI 12.65-14.78) and 13.91 injuries (95%CI 12.89-14.99) per 1000 athlete-days for female and male athletes, respectively, with an incidence rate ratio of 0.98 (95% CI 0.88-1.10). The CG had a higher rate of injuries, specifically gradual onset injuries compared to the Olympic Games (OG) and Youth Olympic Games (YOG). The lack of standardization in the current medical records does not allow for consistent coding of minimal recommended injury surveillance data. This may explain the significant difference in injury rates reported at the CG compared to the OG and YOG. A new injury surveillance system that is up to international standards may assist in more accurate reporting and comparison across similar studies.
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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 | 0.040 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".