Online information and availability of three doping substances (anabolic agents) in sports: role of pharmacies
Bibliographic record
Abstract
Background: The Internet has become an important source for easy access to doping substances, where people and athletes may acquire, outside pharmacies and without a (medical) prescription. These online websites do not always offer quality-assured products, and are outside the regular distribution channels of medicines. The aim of this study was to estimate the availability and accessible information on the Internet about the sale of three doping substances (oxandrolone, DHEA, androstenedione). Methods: Cross-sectional exploratory study, being an observation at a point in time of the online availability of these three doping substances (WADA S1 category: anabolic agents), purchased from Spain, Puerto Rico, Canada, United States, Ukraine and Russia. The characteristics of the websites, the countries the webs sold to, the pharmaceutical forms offered and the recommendations for its use were analyzed by using a computer tool designed ad hoc. Results: There were significant differences between countries in the number of webpages that sold the products (Chi-square test, p < 0.05). Oxandrolone was available for purchase mainly when buying from Spain (27.12%) and Ukraine (26.58%), in websites dedicated to sports (77.26%). For DHEA, most of the pages offered it if the search was done from Canada (23.34%) and Russia (21.44%). Products containing androstenedione or DHEA are claimed to enhance sports performance or for sports use without providing details. Compared to the total number of websites checked, the proportion of pharmacies offering these products was low, ranging from 4.86% for DHEA to 15.79% for androstenedione. Conclusion: The three substances selected are easily available without control through the Internet. Only a small number of websites offering them were online pharmacies, and requested a prescription. Most of the doping substances are purchased from the country where they are requested. Product information described benefits for sports performance, but did not do the same with their side effects. It would be advisable for these products to be sold through pharmacies, to guarantee their quality and provide evidence-based information on their safe use, benefits and risks, and only with a prescription. Athletes should be encouraged to consult health professionals about those supplements suitable for their type of training and sports objectives.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".