The role of banned substance residue analysis in the control of dietary supplement contamination
Bibliographic record
Abstract
The potential for contaminated dietary supplements to result in a failed doping test remains a concern for athletes, trainers, and sporting authorities despite improvements to regulatory guidelines. Previous surveys of readily available supplements confirm that many are contaminated with steroids and stimulants prohibited for use in elite sport. Suggested responses to this issue include the complete avoidance of all supplements. Many athletes, however, use nutritional supplements to achieve effective training and also to ensure that daily nutritional requirements are met (e.g. recommended levels of vitamins and minerals). This ensures that the use of supplements is and will remain the norm for a range of sports. As a result, an alternative approach of rigorous testing of materials destined for use by elite athletes has been introduced in several countries. While the testing of final product for banned substances may help mitigate the problem, it will not help to remove the underlying issue of contamination. In this article we describe an alternative approach that uses appropriate quality assurance procedures backed up by testing to remove sources of contamination. The decrease in the incidence of contamination amongst supplement companies adopting such a system is explained, and contrasted with the relatively high incidences of contamination found in products that are not part of a quality system. These findings are of key importance to both supplement manufacturers and those involved in advising athletes about supplement use.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".