High precompetition injury rate dominates the injury profile at the Rio 2016 Summer Paralympic Games: a prospective cohort study of 51 198 athlete days
Bibliographic record
Abstract
OBJECTIVES: To describe the incidence of injury in the precompetition and competition periods of the Rio 2016 Summer Paralympic Games. METHODS: A total of 3657 athletes from 78 countries, representing 83.4% of all athletes at the Games, were monitored on the web-based injury and illness surveillance system over 51 198 athlete days during the Rio 2016 Summer Paralympic Games. Injury data were obtained daily from teams with their own medical support. RESULTS: A total of 510 injuries were reported during the 14-day Games period, with an injury incidence rate (IR) of 10.0 injuries per 1000 athlete days (12.1% of all athletes surveyed). The highest IRs were reported for football 5-a-side (22.5), judo (15.5) and football 7-a-side (15.3) compared with other sports (p<0.05). Precompetition injuries were significantly higher than in the competition period (risk ratio: 1.40, p<0.05), and acute traumatic injuries were the most common injuries at the Games (IR of 5.5). The shoulder was the most common anatomical area affected by injury (IR of 1.8). CONCLUSION: The data from this study indicate that (1) IRs were lower than those reported for the London 2012 Summer Paralympic Games, (2) the sports of football 5-a-side, judo and football 7-a-side were independent risk factors for injury, (3) precompetition injuries had a higher IR than competition period injuries, (4) injuries to the shoulder were the most common. These results would allow for comparative data to be collected at future editions of the Games and can be used to inform injury prevention programmes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".