Utilization of Imaging for Severe Injuries at the Beijing 2022 Winter Olympic Games
Bibliographic record
Abstract
Purpose: In order to better understand the imaging of severe trauma in sport, this study describes the imaging modalities utilized to image athletes who experienced severe traumatic injuries at the Beijing Winter Olympic Games 2022, the distribution of these modalities in relation to the sporting facilities, and the types of injuries imaged in each sport. Methods: This is a retrospective analysis with descriptive tables and figures, performed on a single population (athletes of the Beijing Winter Olympic Games 2022). Results: Of the 2871 athletes in the Beijing Winter Olympic Games, there were 40 athletes with severe injuries who underwent medical imaging. MRI was used more often than Radiography or CT. Athletes at venues without MRI on site had to be transferred to adjacent hospitals for care. Alpine and Freestyle skiing athletes experienced the majority of severe traumatic injuries at this Olympic Games, and the majority of injuries were to the lower limb. Conclusions: Access to medical imaging for severely injured athletes is a critical consideration in the organization of any sporting event. MRI in particular is highly utilized in this population.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".