Strategies for maintaining penile size following penile implant.
Bibliographic record
Abstract
INTRODUCTION: Loss of penile size is a common complaint that can negatively affect patient satisfaction rates following successful penile prosthetic implant surgery. OBJECTIVE: The aim of this review is to describe the various strategies that have been used to maintain penile length or girth after the insertion of a penile prosthetic implant. METHODS: An extensive systematic literature review was performed, based on a search of the PUBMED database for articles published between 2002 to 2012. The following key words were used: penile prosthesis, implant, penile length, size, penis, enhancement, enlargement, phalloplasty, girth, lengthening, and augmentation. Only English-language articles that were related to penile prosthetic surgery and penile size were sought. DISCUSSION: Based on the results of our search, strategies were classified into 3 groups based on the timepoint in relation to the primary penile prosthetic insertion surgery, which included pre-insertion, intraoperative and post-insertion. CONCLUSIONS: Strategies to preserve and potentially increase penile size are of great importance to all implanters. Besides traction therapies and surgeries to enhance perceived penile size, refinements in the surgical approach are simple ways to optimize penile length. A direct comparison of treatment outcomes evaluating the various approaches is not currently possible, owing to divergent study techniques. The implanting surgeon can best serve his patient by adopting a combination of different strategies that are individualized and specific to the patient's needs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".