Sports injuries and illnesses at the Lausanne 2020 Youth Olympic Winter Games: a prospective study of 1783 athletes from 79 countries
Bibliographic record
Abstract
OBJECTIVE: To describe the injury and illness characteristics among participating athletes during the Lausanne 2020 Youth Olympic Winter Games (YOG 2020), 9-22 January 2020. METHODS: The daily number of athlete injuries and illnesses were recorded (1) through the reporting of all National Olympic Committee (NOC) medical teams and (2) in the polyclinic and medical venues by the Lausanne 2020 medical staff. RESULTS: In total, 1783 athletes from 79 NOCs were observed. NOC and Lausanne 2020 medical staff reported 228 injuries and 167 illnesses, equating 11.7 injuries and 8.6 illnesses per 100 athletes over the 14-day period. Injury incidence was highest for snowboard slopestyle (39%), bobsleigh (36%), snowboard big air (29%), ski slopestyle (29%), snowboard cross (24%) and ski cross (21%), and lowest for speed skating, snowboard halfpipe and curling (2%-4%). The highest incidence of illness was recorded for curling (21%), ski mountaineering (15%), snowboard halfpipe (13%), bobsleigh (11%), cross-country skiing (10%) and figure skating (10%). Almost one-third of injuries were expected to result in time loss and 17% of illnesses. Most injuries occurred to the knee (12%) and head (11%), and 64% of illnesses affected the respiratory system. Overall, women suffered more injuries and illness than males. CONCLUSION: Overall, injury and illness rates were similar compared with recent YOG. While the rate and characteristics of injury and illness varied between sports, consistent patterns across YOG are emerging. If addressed, changes in highlighted areas of risk could have a positive impact on the health and well-being of these young 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".