Self-reported sports injuries and later-life health status in 3357 retired Olympians from 131 countries: a cross-sectional survey among those competing in the games between London 1948 and PyeongChang 2018
Bibliographic record
Abstract
OBJECTIVE: Describe the self-reported prevalence and nature of Olympic-career injury and general health and current residual symptoms in a self-selected sample of retired Olympians. METHODS: 1 month), general health (eg, depression) during the athlete's career, and current musculoskeletal pain and functional limitations. RESULTS: 55% were men (44% women, 1% unknown), representing 57 sports (42 Summer, 15 Winter), aged 44.7 years (range 16-97). A total of 3746 injuries were self-reported by 2116 Olympians. This equated, 63.0% (women 68.1%, men 59.2%) reporting at least one significant injury during their Olympic career. Injury prevalence was highest in handball (82.2%) and lowest in shooting (40.0%) for Summer Olympians; and highest in alpine skiing (82.4%) and lowest in biathlon (40.0%) for Winter Olympians. The knee was the most frequently injured anatomical region (20.6%, 120 median days severity), followed by the lumbar spine (13.1%, 100 days) and shoulder/clavicle (12.9%, 92 days). 6.6% of Olympians said they had experienced depression during their career. One-third of retired Olympians reported current pain (32.4%) and functional limitations (35.9%). CONCLUSIONS: Almost two-thirds of Olympians who completed the survey reported at least one Olympic-career significant injury. The knee, lumbar spine and shoulder/clavicle were the most commonly injured anatomical locations. One-third of this sample of Olympians attributed current pain and functional limitations to Olympic-career injury.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".