Penile Modeling in Peyronie's Disease: A Systematic Review of the Literature
Bibliographic record
Abstract
INTRODUCTION: Penile modeling to correct the penile curvature in Peyronie's disease (PD) may be achieved manually (intra-operatively or post-injection) or by using assisted devices (penile traction, vacuum device, or penile prosthesis). OBJECTIVES: To evaluate the efficacy, safety, and satisfaction associated with penile modeling in patients with PD. METHODS: A PROSPERO registered (CRD42021241729) systematic search in MEDLINE and Cochrane Library was done following PRISMA. PICO: Studies were deemed eligible if they assessed patients with PD (P) undergoing modeling procedures (I) with or without a comparative group(C) evaluating the efficacy, safety, or patient satisfaction (O). Retrospective and prospective primary studies were included. The primary outcome measure is the change in penile curvature after modeling. The secondary outcome measures are the change in stretched penile length, adverse events, and patient satisfaction after modeling. RESULTS: A total of 23 studies, involving 1,238 patients were included. Most studies (13, 56.5%) evaluated penile traction therapy. The studies were of low and intermediate quality (mean Newcastle-Ottawa Scale score of 5.7 and mean Jadad score of 3.3) with a mean level of evidence of 3.4. The mean penile curvature at baseline was between 31 and 80.8 degrees. Nine (39.1%) studies found a significant improvement (P < .05) of penile curvature after penile modeling, ranging between 11.7, and 37.2 degrees. An increase in mean stretched penile length was reported in 7 (30.4%) articles, varying between 0.4, and 1.8 cm. Serious complications such as penile prosthesis malfunctions (3.3-11.1%) and urethral injuries (2.9%) were only reported for intra-operative manual modeling. CONCLUSION: Although individual studies have noted improvement in penile curvature and stretched penile length, specific recommendations regarding penile modeling in PD cannot be provided due to limited evidence available. Further RCTs with adequate sample size, validated assessment tools, and longer follow-up are needed. Krishnappa P, Manfredi C, Sinha M et al. Penile Modeling in Peyronie's Disease: A Systematic Review of the Literature. Sex Med Rev 2022;10:427-443.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| 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) | 0.004 | 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".