Sexual Enhancement Products for Sale Online: Raising Awareness of the Psychoactive Effects of Yohimbine, Maca, Horny Goat Weed, and<i>Ginkgo biloba</i>
Bibliographic record
Abstract
INTRODUCTION: The use of unlicensed food and herbal supplements to enhance sexual functions is drastically increasing. This phenomenon, combined with the availability of these products over the Internet, represents a challenge from a clinical and a public health perspective. METHODS: A comprehensive multilingual assessment of websites, drug fora, and other online resources was carried out between February and July 2013 with exploratory qualitative searches including 203 websites. Additional searches were conducted using the Global Public Health Intelligence Network (GPHIN). Once the active constitutes of the products were identified, a comprehensive literature search was carried out using PsycInfo and PubMed. RESULTS: The most common sexual enhancement products available on the Internet were identified. Their active ingredients included yohimbine, maca, horny goat weed and Ginkgo biloba. These four substances were reported with the occurrence of adverse events and the induction of psychological symptoms, such as mood changes, anxiety, and hallucinations as well as addictive behaviours. CONCLUSIONS: Uncontrolled availability of sexual enhancement products that contain potentially harmful substances is a major public health concern. The possible impact on population health, particularly among subjects with psychiatric disorders, usually at risk for sexual dysfunction, may be significant. This new trend needs to be extensively studied and monitored.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".