Urinary complications after penile inversion vaginoplasty in transgender women: Systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Penile inversion vaginoplasty (PIV) remains the gold standard technique for vaginoplasty, a gender-affirming feminizing surgery, but has been associated with urinary complications; however, there is little literature synthesizing urinary complications after PIV surgery, and there is a need to compile these complications to counsel patients pre- and postoperatively on managing surgical expectations. In this systematic review, we summarize the prevalence of urinary complications following PIV. METHODS: We searched the MEDLINE, EMBASE, CINAHL, and Scopus databases in July 2020. The primary outcome was the prevalence of urinary and surgical complications in patients after penile inversion vaginoplasty. Pooled prevalence was determined from extrapolated data. ROBINS-I tool was used to assess study quality. The study was prospectively registered on PROSPERO (CRD 42020204139). RESULTS: Of 843 unique records, 27 articles were pooled for synthesis, with 3388 patients in total. Overall patient satisfaction ranged from 80-100%. The most common urological complications included poor/splayed stream (11.7%, 95% confidence interval [CI] 5.7-19.3), meatal stenosis (6.9%, 95% CI 2.7-12.7), and irritative symptoms (frequency, urgency, nocturia) (11.5%, 95% CI 2.6-25.1). Other urinary complications included retention requiring catheterization (5.1%, 95% CI 0.3-13.8), incontinence (8.7%, 95% CI 3.4-15.6), urethral stricture (4.6%, 95% CI 1.2-9.8), and urinary tract infection (5.6%, 95% CI 2.7-9.4). Most pooled studies had moderate risk of bias. CONCLUSIONS: The available evidence suggests that there is a low prevalence of urinary complications following PIV. Overall, there is a need for standardization of data in transgender surgical care to better understand surgical outcomes and improve postoperative management.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".