Prevalence of and factors associated with osteoarthritis and pain in retired Olympians compared with the general population: part 1 – the lower limb
Bibliographic record
Abstract
Objectives This study aims (1) to determine the prevalence of lower limb osteoarthritis (OA) and pain in retired Olympians; (2) to identify factors associated with their occurrence and (3) to compare with a sample of the general population. Methods 3357 retired Olympians (median 44.7 years) and 1735 general population controls (40.5 years) completed a cross-sectional survey. The survey captured demographics, general health, self-reported physician-diagnosed OA, current joint/region pain and injury history (lasting >1 month). Adjusted OR (aOR) compared retired Olympians with the general population. Results The prevalence of (any joint) OA in retired Olympians was 23.2% with the knee most affected (7.4%). Injury was associated with increased odds (aOR, 95% CI) of OA and pain in retired Olympians at the knee (OA=9.40, 6.90 to 12.79; pain=7.32, 5.77 to 9.28), hip (OA=14.30, 8.25 to 24.79; pain=9.76, 6.39 to 14.93) and ankle (OA=9.90, 5.05 to 19.41; pain=5.99, 3.84 to 9.34). Increasing age and obesity were also associated with knee OA and pain. While the odds of OA did not differ between Olympians and the general population, Olympians with prior knee and prior hip injury were more likely than controls with prior injury to experience knee (1.51, 1.03 to 2.21 (Olympians 22.0% vs controls 14.5%)) and hip OA (4.03, 1.10 to 14.85 (Olympians 19.1% vs Controls 11.5%)), respectively. Conclusions One in four retired Olympians reported physician-diagnosed OA, with injury associated with knee, hip and ankle OA and pain. Although overall OA odds did not differ, after adjustment for recognised risk factors Olympians were more likely to have knee and hip OA after injury than the general population, suggesting injury is an occupational risk factor for retired Olympians.
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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 | 0.000 |
| 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 | 0.000 |
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".