High incidence of injuries at the Pyeongchang 2018 Paralympic Winter Games: a prospective cohort study of 6804 athlete days
Bibliographic record
Abstract
OBJECTIVE: To describe the epidemiology of sports injury at the Pyeongchang 2018 Paralympic Winter Games. METHODS: 567 athletes from 49 countries were monitored daily for 12 days over the Pyeongchang 2018 Paralympic Winter Games (6804 athlete days). Injury data were obtained daily from teams with their own medical support (41 teams and 557 athletes) and teams without their own medical support (8 teams and 10 athletes) through two electronic data capturing systems. RESULTS: 112 of 567 athletes (19.8%) reported a total of 142 injuries, with an injury incidence rate (IR) of 20.9 per 1000 athlete days (95% CI 17.4 to 25.0). The highest IR was reported for para snowboard (IR of 40.5 per 1000 athlete days [95% CI 28.5 to 57.5]; p<0.02), particularly in the lower limb and head/face/neck anatomical areas. Across all sports at the Games, acute traumatic injuries (IR of 16.2 per 1000 athlete days [95% CI 13.2 to 19.8]) and injuries to the shoulder/arm/elbow complex (IR of 5.7 per 1000 athlete days [95% CI 4.2 to 7.8]) were most common. However, most injuries (78.9%) did not require time loss. CONCLUSION: The new Paralympic Winter Games sport of Para snowboard requires attention to implement actions that will reduce injury risk. The shoulder was the most injured single joint-a consistent finding in elite para sport.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".