Gender affirming penile inversion vaginoplasty and shallow depth vaginoplasty – outcomes based on a shared decision-making model
Bibliographic record
Abstract
Objective To evaluate and compare postoperative outcomes, complications, and patient satisfaction following penile inversion vaginoplasty (PIV) and shallow depth vaginoplasty (SDV) within a single-center gender-affirming surgical program.Summary background data Vaginoplasty is a feminizing gender-affirming surgery (GAS) for transgender and gender diverse (TGD) individuals. PIV is a well-established technique, while SDV provides an alternative for those not desiring a neo-vagina.Methods A retrospective cohort study included 122 patients undergoing PIV (83.6%, n = 102) or SDV (16.4%, n = 20) between 2019 and 2024 at an academic program. Data on demographics, intraoperative and postoperative complications, and patient-oriented outcomes were analyzed. Thematic analysis identified preoperative goals. Statistical tests included Mann-Whitney U, Chi-squared, and multivariable logistic regression.Results SDV patients were older (mean age: 44.7 vs. 33.2 years; p = 0.006) and had more comorbidities. These were not associated with adverse outcomes in the PIV group. Alleviation of gender dysphoria was the most frequently reported surgical goal preoperatively, followed by themes related to sexual function, esthetics, and presence or absence of a vaginal canal. Surgical choice aligned with initial patient preference in 99.2% of cases. Complications were more frequent in PIV than SDV (Clavien-Dindo ≥2, 46% vs. 10%; p < 0.001), wound dehiscence (34.3% vs. 10%; p = 0.034) and hyper-granulation (55% vs. 5%; p < 0.001). Despite shorter follow-up, SDV was associated with fewer short-term complications and comparable satisfaction rates to PIV (95%, 82% p = 1).Conclusions PIV and SDV are both surgeries with comparable safety and satisfaction rates within the 1st year. SDV is a surgical alternative that offers a less complex recovery pathway, pursued by patients for individual gender affirming reasons. Shared decision-making enhances patient autonomy and may contribute to optimizing postoperative outcomes.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".