Discordance Between Online Information and Male Hypogonadism Clinical Guidelines: A Global Multilingual Content Analysis
Bibliographic record
Abstract
CONTEXT: Testosterone prescriptions have increased up to 12-fold globally over the past 2 decades. EU and UK law tightly regulate the advertising of medical products. OBJECTIVE: To review the accuracy of publicly accessible information on websites offering testosterone treatment. DESIGN/SETTING: Content analysis methodology using concept- and data-driven strategies to develop a coding frame for data extraction. Publicly accessible websites offering testosterone prescriptions were identified using predefined search terms, conducted in English, Arabic, Hindi, and Spanish, across 3 search engines. Virtual private network searches within multiple geographical regions were used to reduce location bias. MAIN OUTCOME MEASURE: Accuracy of extracted data determined using international guidelines. RESULTS: A total of 253/1138 websites were included (144 US/Canada; 48 Europe; 17 Australia; 12 Asia; 11 South America; 10 Middle East). The following non-guideline-based practices (with numbers/percentages of clinics) were identified: routinely use nontestosterone androgens or testosterone secretagogues (eg, gonadotrophins) to treat symptomatic low testosterone (61/253; 24.4%); testosterone treatment reduces cardiovascular risk (52/253; 20.6%); microdosing improves treatment effects (30/253; 11.9%); testosterone is prescribed for men with normal serum testosterone (>12 nmol/L; 25/253;9.9%); testosterone has antiaging effects (25/253; 9.9%). US-based clinics more frequently made non-guideline-based claims compared with other geographical locations. CONCLUSION: We identify serious and frequent breaches of advertising law and regulations by clinics around the world offering testosterone treatment, with the potential to cause harm to men. We recommend enforcement of existing laws by national regulators to address this widespread public health challenge and align patient expectations with clinical guidelines for the safe treatment of men.
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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 | 0.048 | 0.202 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".