Comprehensive Descriptive Video Analysis of All Short Track Speed Skating Falls in 16 International Competitions From 2021 to 2023 With Injury Reporting for the Canadian Team
Bibliographic record
Abstract
Short track speed skating (STSS) is an Olympic Winter Sport well known for its high-speed races-often exceeding 50 km/h-on short oval tracks. These extreme speeds and close athlete proximity make falls frequent, which can result in serious injuries. However, no study has comprehensively analyzed falls or fall-related injuries in this sport. The objectives of this study were to describe all fall incidents during international STSS competitions from October 2021 to October 2023 and to identify fall-related injuries sustained by Canadian athletes during these events. Video recordings from 16 International Skating Union and Olympic Games competitions were analyzed by two engineering interns to describe the characteristics of falls-including race type, location and cause of the fall, and body impact zone-among all participating athletes. In addition, the medical records of Canadian STSS athletes were reviewed by the team's physical therapists to document all fall-related injuries sustained by the athletes. Forty-three countries and 18 631 race entries were involved across the competitions. A total of 1505 falls were recorded, averaging 94.1 ± 39.6 falls per competition. Men had a higher fall proportion than women (8.6% vs. 7.1%, p < 0.05). Of the falls, 34.6% were individual and 60.9% were group falls. The Canadian team's injury-per-crash proportion was 7.1%. Fall-related injuries mainly involved strains in the spinal region and contusions on the lower legs. This first comprehensive analysis of falls in STSS revealed a high frequency of falls, with athlete interactions as the primary cause. It also highlighted a higher fall proportion among men and a higher incidence of injury to the spinal region, emphasizing the need for a deeper understanding of injury mechanisms to improve safety and protective measures.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".