Platelet-Rich Plasma Injections for Erectile Dysfunction and Peyronie's Disease: A Systematic Review of Evidence
Bibliographic record
Abstract
INTRODUCTION: Erectile Dysfunction (ED) and Peyronie's Disease (PD) are debilitating medical conditions affecting patients' quality of life (QoL). Platelet-rich plasma (PRP) injections are one of the various emerging approaches proposed to treat these medical conditions. AIM: To describe the evidence of the potential role of PRP injections in ED and PD. METHODS: The authors conducted a systematic review according to the PRISMA statement using the following databases in November 2019: The National Library of Medicine (PubMed), Ovid Medline, Cochrane, Scopus, Embase, and Embase classic. The search was performed using keywords drawn from studies on the use of PRP in ED and PD in clinical and preclinical studies. RESULTS: Eighteen articles met the inclusion criteria for review, including 12 studies on the use of PRP in humans and 6 on the use of PRP in rats. Ten studies reported on the efficacy of PRP in ED exclusively, 7 in PD exclusively and one in both conditions. In humans, 6 and 3 studies showed promising results in PD and ED, respectively. No major complications were noted. Unwanted minor side effects were noted by studies reporting on PD, including mild penile bruising, ecchymosis, hematomas as well as transient hypotension noted in 2 out of 90 patients. CONCLUSION: PRP injections for the treatment of ED may be promising, but no recommendation can be made because of scarce evidence. Safety and effectiveness of this therapy in the treatment of ED and PD require further preclinical and clinical studies with standardized protocols to gain an adequate insight into its potential implications. Patients should be offered to be part of such trials to better understand PRP potential. Alkandari MH, Touma N, Carrier S, Platelet-Rich Plasma Injections for Erectile Dysfunction and Peyronie's Disease: A Systematic Review of Evidence. Sex Med Rev 2022;10:341-352.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".