Bibliographic record
Abstract
The purpose of this study was to determine the rate and type of injury in elite Canadian Taekwondo athletes, before and during competition and to investigate the relationship between past injuries, injuries during competition and success. This retrospective case-series study incorporated Taekwondo injuries sustained by 75 male and female elite Canadian Taekwondo athletes over 10 years and its relationship to athletes' success by means of gaining medals during competition. A logistic regression model (using the Generalised Estimating Equations (GEE) method) was used to investigate the relationship between injuries and success. Injury rate was associated with performance after holding variables constant (Odds Ratio (OR) = 0.124, P = 0.039). Moreover, with each additional injury per match, competitors were 88% (1-0.124) less likely to win a medal. Although not statistically significant, additional injuries prior to competition were associated with a 30% increase in medal prevalence (OR = 1.299, P = 0.203). When comparing athletes (gender, tournament difficulty, injury variables), a competitor who is one year older is 10% less likely to medal (OR = 0.897, P = 0.068). When an additional injury occurred during competition, the athlete was 88% less likely to win a medal. Prevention, correct diagnosis, and immediate therapeutic intervention by qualified health care providers are important.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".