The Epidemiology of Injuries in Football at the London 2012 Paralympic Games
Bibliographic record
Abstract
BACKGROUND: The epidemiology of injury in Paralympic football has received little attention. A study of all sports at the London 2012 Paralympic Games identified football 5-a-side as the sport with the highest injury rate, meriting further detailed analysis, which may facilitate the development of strategies to prevent injuries. OBJECTIVE: To examine the injury rates and risk factors associated with injury in Paralympic football. DESIGN: Secondary analysis of a prospective cohort study of injuries to football 5-a-side and football 7-a-side athletes. SETTING: London 2012 Paralympic Games. PARTICIPANTS: Participants included 70 football 5-a-side athletes and 96 football 7-a-side athletes. Athletes from all but one country chose to participate in this study. METHODS: The Paralympic Injury and Illness Surveillance System was used to track injuries during the Games, with data entered by medical staff. MAIN OUTCOME MEASUREMENTS: Injury incidence rate (IR) and injury incidence proportion (IP). RESULTS: The overall IR for football 5-a-side was 22.4 injuries/1000 athlete-days (95% confidence interval [CI], 14.1-33.8) with an IP of 31.4 injuries per 100 athletes (95% CI, 20.9-43.6). In 5-a-side competition, 62.5% of injuries were associated with foul play. The overall IR for football 7-a-side was 10.4 injuries/1000 athlete-days (95% CI, 5.4-15.5), with an IP of 14.6 injuries per 100 athletes (95% CI, 7.5-21.6). The most commonly injured body region in both sports was the lower extremity. CONCLUSIONS: To our knowledge, this study is the first to examine IR and risk factors associated with injury in Paralympic football. Future studies are needed to determine mechanisms of injury and independent risk factors for injury, thus informing prevention strategies.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".