Investigating iontophoresis as a therapeutic approach for Peyronie’s disease: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Iontophoresis therapy (IPT) is a noninvasive technique that uses electrical impulses to deliver charged molecules into the skin for controlled and targeted drug delivery. IPT has been explored as a noninvasive treatment option for Peyronie's disease (PD), but the current literature in this regard is still scarce. OBJECTIVE: We aimed to systematically review the current literature on the application of IPT in the management of PD to provide a comprehensive evaluation and holistic outlook on the subject. METHOD: A comprehensive search strategy was implemented in the following databases to retrieve research articles: PubMed (MEDLINE), Scopus, and Web of Science. Google Scholar was also manually searched. The search results were imported into Rayyan reference management for assessment based on the predefined inclusion criteria. The quality of the articles was evaluated by the proper JBI checklist (ie, per the study design), and the JBI grades of recommendation were used for grading the evidence. RESULTS: A systematic search yielded 451 publications, 11 of which met the criteria to be included in this systematic review. The results demonstrated that IPT, usually with verapamil and dexamethasone, has shown promising results in treating PD. These methods can reduce pain, plaque size, and penile curvature while improving sexual function and quality of life with no serious adverse events. However, most studies had moderate to low quality, indicating a weak recommendation for a certain health management strategy. CONCLUSION: Based on the extant literature, there is currently insufficient evidence to support the use of IPT for the management of PD. Placing it in the forefront of research can facilitate the management choices for PD even further, given its therapeutic potential.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".