Hormone abuse in sports: the antidoping perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it