The IOC Centres of Excellence bring prevention to Sports Medicine
Bibliographic record
Abstract
The protection of an athlete's health and preventing injuries and illnesses in sport are top priorities for the IOC and its Medical Commission. The IOC therefore partners with selected research centres around the world and supports research in the field of sports medicine. This has enabled the IOC to develop an international network of expert scientists and clinicians in sports injury and disease prevention research. The IOC wants to promote injury and disease prevention and the improvement of physical health of the athlete by: (1) establishing long-term research programmes on injury and disease prevention (including studies on basic epidemiology, risk factors, injury mechanisms and intervention), (2) fostering collaborative relationships with individuals, institutions and organisations to improve athletes' health, (3) implementing and collaborating with applied, ongoing and novel research and development within the framework and long-term strategy of the IOC and (4) setting up knowledge translation mechanisms to share scientific research results with the field throughout the Olympic Movement and sports community and converting these results into concrete actions to protect the health of the athletes. In 2009, the IOC also identified four research centres that had an established track record in research, educational and clinical activities to achieve these ambitions: (1) the Australian Centre for Research into Injury in Sport and its Prevention (ACRISP), Australia; (2) the Sport Injury Prevention Research Centre (SIPRC), Canada; (3) the Clinical Sport and Exercise Medicine Research (CSEM), South Africa and (4) the Oslo Sports Trauma Research Center (OSTRC), Norway. This paper highlights the work carried out by these four IOC Centres of Excellence over the past 6 years and their contribution to the world of sports medicine.
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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 | 0.049 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.034 | 0.009 |
| Open science | 0.009 | 0.045 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.079 | 0.048 |
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".