No pain no gain? A conversation on Olympians’ long-term health
Bibliographic record
Abstract
Willie Banks OLY is smiling, he usually is. Olympian, former world record holder and world champion. Inventor of the now ubiquitous track and field competitor overhead hand clap. Successful businessman and international sports leader. Life has been good thanks to elite-level sport. There’s just one snag, Willie has osteoarthritis in his knees and hips. He is in constant pain and attributes his osteoarthritis to past sporting injuries. Willie is not alone. Olympians and former elite athletes often link their past sporting injuries to their current musculoskeletal pain. There are many benefits to a life devoted to elite sport, with numerous studies reporting the positive effects of sport including a lower risk of morbidity, better self-reported health in later life and higher quality of life.1–4 Despite this, major injuries including anterior cruciate ligament tears and substantial meniscal tears can contribute to radiographic and symptomatic osteoarthritis,5–8 and the risk of osteoarthritis in elite athletes may be higher than that in the general population.4 This reality raises questions. Is enough being done to educate and support athletes and their entourages so they can minimise the long-term …
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.018 | 0.037 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".