Imaging-detected bone stress injuries at the Tokyo 2020 summer Olympics: epidemiology, injury onset, and competition withdrawal rate
Bibliographic record
Abstract
BACKGROUND: Prevention and early detection of injuries are essential in optimising sport participation and performance. The aim of this study is to investigate the epidemiology, athlete injury history, and competition withdrawal rate of imaging-detected bone stress injuries during the Tokyo 2020 Olympic Games. METHODS: We collected and analysed imaging and clinical information in athletes with bone stress injuries diagnosed in the Olympic Village polyclinic during the Games. Two physicians independently and retrospectively reviewed all imaging examinations of bone stress injuries. RESULTS: A total of 11,315 individual athletes from 206 National Olympic Committees competed at the Games, during which 567 MRIs and 352 X-rays were performed at the Olympic Village polyclinic. Radiology examinations revealed four stress fractures and 38 stress reactions in 29 athletes (median age 24 years, range 18-35 years). Of these, 72% of athletes (n = 21) had symptoms before entering the Olympic Village. Bone stress injuries were most common in women (55%), the lower extremities (66%), and track and field athletes (45%). Six athletes (21%) did not start or did not finish their competitions. CONCLUSIONS: This study revealed 42 imaging-detected bone stress injuries in the polyclinic of the Tokyo 2020 Olympic Village. The high proportion of athletes with symptoms before entering the village and the high proportion of competition withdrawals suggests the usefulness of an early MRI examination.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".