Epidemiology of MRI-detected muscle injury in athletes participating in the Tokyo 2020 Olympic Games
Bibliographic record
Abstract
OBJECTIVE: Muscle injury is one of the most common injuries occurring at the Olympic Games often with devastating consequences. Epidemiological injury surveillance is recognised by the IOC as essential for injury prevention and management. We aimed to describe the incidence, anatomical location and classification of MRI-detected muscle injuries in athletes who participated in the Tokyo 2020 Olympic Games. METHODS: Two board-certified orthopaedic surgeons, highly experienced in reviewing MRIs, independently and retrospectively reviewed all MRIs collected at the Tokyo 2020 Olympic Games from clinical reports generated by board-certified musculoskeletal radiologists at the IOC Polyclinic. The presence and anatomical site of muscle injuries were classified as: type a: myofascial/peripheral; type b: muscle belly or musculotendinous junction; and type c: injury which extends into the tendon, with reference to the British Athletics Muscle Injury Classification. RESULTS: Fifty-nine MRI-detected muscle injuries were seen in 40 male and 19 female athletes. 24 athletes (41%) were unable to fully compete in their event. Fifty-two injuries (88%) involved lower extremity muscles with hamstring muscle injuries most common (32 of 59, 54%). Half of all muscle injuries occurred in athletes participating in athletics (30 of 59, 51%). 21 athletes (35%) sustained type a injuries, 14 athletes (24%) type b injuries and 24 athletes (41%) type c injuries. Of athletes with type c injuries, 18 (75%) did not complete their competition, a rate significantly higher than types a and b (OR 14.50, 95% CI 4.0 to 51.9, p<0.001). CONCLUSION: For athletes sustaining muscle injuries during the Olympic Games, our study demonstrates the prognostic relevance of muscle injury anatomical site and severity for predicting completion or non-completion of an Olympic athlete's competition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".