How do the new Olympic sports compare with the traditional Olympic sports? Injury and illness at the 2018 Youth Olympic Summer Games in Buenos Aires, Argentina
Bibliographic record
Abstract
OBJECTIVE: To describe injuries and illnesses across traditional and new sports among the participating athletes of the Buenos Aires 2018 Youth Olympic Summer Games (BA YOG) (6-18 October 2018). METHODS: We recorded the daily number of athlete injuries and illnesses (1) through the reporting of all National Olympic Committee (NOC) medical teams and (2) in the polyclinic and medical venues manned by the BA YOG 2018 medical staff. RESULTS: In total, 3.984 athletes from 206 NOCs were observed. NOCs and BA YOG 2018 medical staff reported 619 injuries and 334 illnesses, equalling 15.5 injuries and 8.4 illnesses per 100 athletes over the 13-day period. The eight new sports on the Youth Olympic programme (futsal, beach handball, karate, roller speed skating, kitesurfing, BMX freestyle, climbing and break dancing) fell in between the other sports with respect to injury and illness risk. Injury incidence was highest in rugby (43% of all rugby players), followed by boxing (33%) and badminton (24%), and lowest in swimming, archery, roller speed skating, equestrian, climbing and rowing (<5%). The highest incidences of illness were recorded in golf (20%), followed by triathlon (16%), beach volleyball and diving (both 14%). Of the illnesses, 50% affected the respiratory system and 15% the gastrointestinal system. Injury and illness incidences varied between continents with athletes representing Europe having significantly fewer injuries and illnesses compared with other continents, apart from a similar illness incidence to Asian athletes. CONCLUSION: The overall injury incidence of 15.5 injuries per 100 athletes was higher, while the overall illness incidence of 8.4 illnesses per 100 athletes was similar to previous youth and Olympic Games. The new sports did not differ significantly compared with the other sports with respect to injury and illness risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".