Hormone abuse in sports: the antidoping perspective
Bibliographic record
Abstract
Since ancient times, unethical athletes have attempted to gain an unfair competitive advantage through the use of doping substances. A list of doping substances and methods banned in sports is published yearly by the World Anti-Doping Agency (WADA). A substance or method might be included in the List if it fulfills at least two of the following criteria: enhances sports performance; represents a risk to the athlete's health; or violates the spirit of sports. This list, constantly updated to reflect new developments in the pharmaceutical industry as well as doping trends, enumerates the drug types and methods prohibited in and out of competition. Among the substances included are steroidal and peptide hormones and their modulators, stimulants, glucocorticosteroids, beta2-agonists, diuretics and masking agents, narcotics, and cannabinoids. Blood doping, tampering, infusions, and gene doping are examples of prohibited methods indicated on the List. From all these, hormones constitute by far the highest number of adverse analytical findings reported by antidoping laboratories. Although to date most are due to anabolic steroids, the advent of molecular biology techniques has made recombinant peptide hormones readily available. These substances are gradually changing the landscape of doping trends. Peptide hormones like erythropoietin (EPO), human growth hormone (hGH), insulin, and insulin-like growth factor I (IGF-I) are presumed to be widely abused for performance enhancement. Furthermore, as there is a paucity of techniques suitable for their detection, peptide hormones are all the more attractive to dishonest athletes. This article will overview the use of hormones as doping substances in sports, focusing mainly on peptide hormones as they represent a pressing challenge to the current fight against doping. Hormones and hormones modulators being developed by the pharmaceutical industry, which could emerge as new doping substances, are also discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".