Regenerative Therapy in Erectile Dysfunction: A Survey on Current Global Practice Trends and GAF Expert Recommendations
Bibliographic record
Abstract
PURPOSE: This study aimed to examine current global practices in regenerative therapy (RT) for erectile dysfunction (ED) and to establish expert recommendations for its use, addressing the current lack of solid evidence and standardized guidelines. MATERIALS AND METHODS: A 39-question survey was developed by senior Global Andrology Forum (GAF) experts to comprehensively cover clinical aspects of RT. This was distributed globally via a secure online Google Form to ED specialists through the GAF website, international professional societies, and social media, the responses were analyzed and presented for frequencies as percentages. Consensus on expert recommendations for RT use was achieved using the Delphi method. RESULTS: Out of 479 respondents from 62 countries, a third reported using RT for ED. The most popular treatment was low-intensity shock wave therapy (54.6%), followed by platelet-rich plasma (24.5%) and their combination (14.7%), with stem cell therapy being the least used (3.7%). The primary indication for RT was the refractory or adverse effects of PDE5 inhibitors, with the best effectiveness reported in middle-aged and mild-to-moderate ED patients. Respondents were confident about its overall safety, with a significant number expressing interest in RT's future use, despite pending guidelines support. CONCLUSIONS: This inaugural global survey reveals a growing use of RT in ED treatment, showcasing its diverse clinical applications and potential for future widespread adoption. However, the lack of comprehensive evidence and clear guidelines requires further research to standardize RT practices in ED treatment.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".