Sports injury and illness incidence in the PyeongChang 2018 Olympic Winter Games: a prospective study of 2914 athletes from 92 countries
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To describe the incidence of injuries and illnesses sustained during the XXIII Olympic Winter Games, hosted by PyeongChang on 9-25 February 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 by the PyeongChang 2018 medical staff. RESULTS: In total, 2914 athletes (1210 women, 42%; 1704 men, 58%) from 92 NOCs were observed for occurrence of injury and illness. NOC and PyeongChang 2018 medical staff reported 376 injuries and 279 illnesses, equalling 12.6 injuries and 9.4 illnesses per 100 athletes over the 17-day period. Altogether, 12% of the athletes incurred at least one injury and 9% at least one illness. The injury incidence was highest in ski halfpipe (28%), snowboard cross (26%), ski cross (25%), snowboard slopestyle (21%) and aerials (20%), and lowest in Nordic combined, biathlon, snowboard slalom, moguls and cross-country skiing (2%-6%). Of the 376 injuries recorded, 33% and 13% were estimated to lead to ≥1 day and >7 days of absence from sport, respectively. The highest incidences of illness were recorded in biathlon (15%), curling (14%), bobsleigh (14%) and snowboard slalom (13%). Thirty per cent of the illnesses were expected to result in time loss, and 70% affected the respiratory system. Women suffered 61% more illnesses than men. CONCLUSION: Overall, 12% of the athletes incurred at least one injury during the Games and 9% an illness, incidences that are similar to the Olympic Winter Games of 2010 and 2014.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it