Muscle injuries in athletics during the 2020 Tokyo Olympic Games: differences between heats and finals
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to analyze muscle injuries and their related risk factors during the Athletics events of the 2020 Tokyo Olympic Games including the differences in muscle injury rates between heats and finals. METHODS: We included and analyzed in this study muscle injuries diagnosed by either magnetic resonance imaging, ultrasound, or physical examinations by at least two physicians, from Athletics athletes participating at the 2020 Tokyo Olympic Games. Data from electronic medical records, including sex, nationality, event, and the round (heat vs. final) during which the muscle injury occurred and the air temperature in the stadium, measured every five minutes during the competition were extracted. RESULTS: Among the 1631 athletes who competed, a total of 36 athletes (20 males and 16 females) were diagnosed with a muscle injury during the 2020 Tokyo Olympic Games. Among them, 24 occurred during heats (1.47 per 100 athletes) and 12 during finals (2.20 per 100 athletes) (P=0.25). Logistic regression analysis revealed that the geographic region of athletes' origin was a factor associated with muscle injury, with the highest muscle injury rate being in athletes from Africa (odds ratio [OR]=4.74, 95% confidence interval [CI]) = 1.75 to 12.82) and North America (OR=3.02, 95%CI=1.27 to 7.20). For male athletes, competing in finals was a risk factor to sustain a muscle injury (OR=2.55, 95%CI=1.01 to 6.45). CONCLUSIONS: During the 2020 Olympic Games, muscle injury rate was higher in finals than in heats, reaching statistical significance in male athletes.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".