Noteworthy Performance of Muscle-Injured Para-Athletes
Bibliographic record
Abstract
BACKGROUND: To the best of our knowledge, no studies have attempted to correlate athletic performance with muscle injuries sustained during Paralympic Games. AIM: This study reports the incidence, anatomical location, anatomical site classification, and relationship between competition results and anatomical site classification in athletes who participated in the Paralympic Games. METHODS: All magnetic resonance images collected at the International Paralympic Committee polyclinic at the Tokyo 2020 Paralympic Games were reviewed to identify the presence and anatomical site of muscle injuries. The athletes' competition results were reviewed using IPC data sources. RESULTS: Twenty-six magnetic resonance imaging-detected muscle injuries were observed in 16 male and 10 female athletes. Muscle injuries were most commonly observed during track and field events ( n = 20) and in athletes with visual impairment ( n = 12). Ten of the injuries involved the tendon. Twenty-one of injured athletes (81%) completed their competition, whereas five athletes did not. Eight athletes won medals in the games. The anatomical site of muscle injury did not significantly impact the proportion of athletes who did not finish competition. CONCLUSIONS: Many athletes who sustained muscle injuries completed their competitions. No association was found between anatomical site classification and Paralympic athletes' performance in this study.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".