Ethics of genetic testing and research in sport: a position statement from the Australian Institute of Sport
Bibliographic record
Abstract
As Australia's peak high-performance sport agency, the Australian Institute of Sport (AIS) has developed this position statement to address the implications of recent advances in the field of genetics and the ramifications for the health and well-being of athletes. Genetic testing has proven of value in the practice of clinical medicine. There are, however, currently no scientific grounds for the use of genetic testing for athletic performance improvement, sport selection or talent identification. Athletes and coaches should be discouraged from using direct-to-consumer genetic testing because of its lack of validation and replicability and the lack of involvement of a medical practitioner in the process. The transfer of genetic material or genetic modification of cells for performance enhancement is gene doping and should not be used on athletes. There are, however, valid roles for genetic research and the AIS supports genetic research which aims to enhance understanding of athlete susceptibility to injury or illness. Genetic research is only to be conducted after careful consideration of a range of ethical concerns which include the provision of adequate informed consent. The AIS is committed to providing leadership in delivering an ethical framework that protects the well-being of athletes and the integrity of sport, in the rapidly changing world of genomic science.
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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 | 0.159 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.045 | 0.047 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".