059 Olympic-career related sports injury epidemiology: the retired olympian musculoskeletal health study (ROMHS)
Bibliographic record
Abstract
Background There are numerous studies describing elite athlete injury patterns seasonally and during major sporting events, however little is known about injury patterns during an elite athlete’s entire sporting career. Objective To describe Olympic-career related significant (≥30 days duration) injuries. Design Cross-sectional survey. Setting The survey was promoted and distributed in eight languages, worldwide via email and social media to Olympians who competed at a Summer and/or Winter Olympic Games and considered themselves retired from Olympic level training and competition. Patients (or Participants) 3,357 Olympians (44% female), median age 44.7 yrs (16–97) from 131 countries and 57 Olympic Sports (42 summer, 15 winter), mean 1.6±0.9 Olympic Games per Olympian. Interventions (or Assessment of Risk Factors) Olympic-career participation and significant injury history. Main Outcome Measurements Injury prevalence by sport and anatomical region. Results There were 3,746 injuries reported in 2,116 Olympians equating to 63.0% of Olympians (female 68.1%, male 59.2%; Summer 62.0%, Winter 69.0%) reporting at least one significant Olympic-career related injury. Overall, 1.1 significant injuries per Olympic-career were reported, with 63.8% (n=2389) of injuries occurring in training. By sport (Summer and Winter, respectively), injury prevalence was highest in handball (82.2%), badminton (78.4%) and judo (77.2%), and alpine skiing (82.4%), freestyle skiing (81.6%), and snowboarding (77.3%), and lowest for shooting (40.0%) and swimming (48.5%), and biathlon (40.0%) and curling (54.3%) (sports with n≥20 participants). The knee (20.6%), followed by the lumbar spine (13.1%), and shoulder (12.9%) were the most common affected injury locations. Conclusions Overall, almost two thirds of Olympians reported sustaining at least one significant Olympic-career related injury. Similar to prospective injury studies, injury prevalence varied across sports, with the knee, lumbar spine and shoulder most commonly affected. It is important to understand the nature and causes of injuries during the entire career of an elite athlete, in order to better inform injury prevention and future athlete health initiatives.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".