Prevalence of and factors associated with osteoarthritis and pain in retired Olympians compared with the general population: part 2 – the spine and upper limb
Bibliographic record
Abstract
OBJECTIVES: (1) To determine the prevalence of spine and upper limb osteoarthritis (OA) and pain in retired Olympians; (2) identify risk factors associated with their occurrence and (3) compare with a sample of the general population. METHODS: 3357 retired Olympians (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 significant injury (lasting ≥1 month). Adjusted ORs (aORs) compared retired Olympians and the general population. RESULTS: Overall, 40% of retired Olympians reported experiencing current joint pain. The prevalence of lumbar spine pain was 19.3% and shoulder pain 7.4%, with lumbar spine and shoulder OA 5.7% and 2.4%, respectively. Injury was associated with increased odds (aOR, 95% CI) of OA and pain at the lumbar spine (OA=5.59, 4.01 to 7.78; pain=4.90, 3.97 to 6.05), cervical spine (OA=17.83, 1.02 to 31.14; pain=9.41, 6.32 to 14.01) and shoulder (OA=4.91, 3.03 to 7.96; pain=6.04, 4.55 to 8.03) in retired Olympians. While the odds of OA did not differ between Olympians and the general population, the odds of lumbar spine pain (1.44, 1.20 to 1.73), the odds of shoulder OA after prior shoulder injury (2.64, 1.01 to 6.90) and the odds of cervical spine OA in female Olympians (2.02, 1.06 to 3.87) were all higher for Olympians compared with controls. CONCLUSIONS: One in five retired Olympians reported experiencing current lumbar spine pain. Injury was associated with lumbar spine, cervical spine and shoulder OA and pain for Olympians. Although overall OA odds did not differ, after adjustment for recognised risk factors, Olympians were more likely to have lumbar spine pain and shoulder OA after shoulder injury, than the general population.
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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) | 0.001 | 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 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".