Outcomes of penile inversion vaginoplasty and robotic‐assisted peritoneal flap vaginoplasty in obese and nonobese patients
Bibliographic record
Abstract
Abstract Aim To explore the impact of body mass index (BMI) on the outcomes of gender‐affirming vaginoplasty. Methods A cohort consisting of all gender‐affirming vaginoplasties in our practice between September 27th, 2018, and September 1st, 2022 were identified, and data were retrospectively collected. Patients were classified as obese if their BMI was ≥30 kg/m 2 at the time of surgery and nonobese if their BMI was <30 kg/m 2 . Complications were assigned a Clavien–Dindo grade and grouped as Grade ≥2 versus Grade ≤1. Patient‐reported functional outcomes of intact erogenous sensation, tactile sensation, ability to achieve penetrative vaginal intercourse, and cosmetic satisfaction were assessed. Results A total of 58 patients with a mean follow‐up time of 6.9 months were included. Seventeen patients (mean BMI = 36.8 kg/m 2 ) were classified as obese and 41 patients (mean BMI = 25.1 kg/m 2 ) were classified as nonobese. No significant differences in outcomes were identified in the obese versus nonobese groups in terms of the incidence of complications. Patient‐reported functional outcomes did not differ significantly between the two groups. Conclusions Similar results can be achieved with gender‐affirming vaginoplasty in obese patients when compared to their nonobese counterparts. Eligibility for this procedure should not be restricted based on BMI alone.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".