The Injury Experience at the 2010 Winter Paralympic Games
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine incidence proportion and the characteristics of athlete injuries sustained during the 2010 Vancouver Paralympic Games. DESIGN: Descriptive epidemiological study. SETTING: All medical venues at the 2010 Vancouver Paralympic Games, Canada. PARTICIPANTS: A total of 505 athletes from 44 National Paralympic Committees participating in the 2010 Vancouver Winter Paralympic Games. ASSESSMENT OF RISK FACTORS: Baseline covariates included sport specificity (ie, ice sledge hockey, alpine skiing, Nordic skiing, wheelchair curling), gender, age, and disability classification. MAIN OUTCOME MEASURES: All injuries that occurred during the 2010 Vancouver Paralympic Games. "Injury" was defined as any sport-related musculoskeletal complaint that caused the athlete to seek medical attention during the study period, regardless of the athlete's ability to continue with training or competition. RESULTS: The Injury Surveillance System identified a total of 120 injuries among 505 athletes [incidence proportion = 23.8% (95% confidence interval, 20.11-27.7)] participating in the 2010 Winter Paralympic Games. There was a similar injury incidence proportion among male (22.8%) and female (26.6%) athletes [incidence rate ratio = 1.1 (95% confidence interval, 0.7-1.7)]. Medical encounters for musculoskeletal complaints were generated in 34% of all sledge hockey athletes, 22% of alpine ski racers, 19% of Nordic skiers, and 18% of wheelchair curling athletes. CONCLUSIONS: The Injury Surveillance System identified sport injuries in 24% of all athletes participating in the 2010 Winter Paralympic Games. The injury risk was significantly higher than during the 2002 (9.4%) and 2006 (8.4%) Winter Paralympic Games. This may reflect improved data collection systems but also highlights the high risk of acute injury in alpine skiing and ice sledge hockey at Paralympic Games. These data will assist future Organizing Committees with the delivery of medical care to athletes with a disability and guide future injury prevention research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".