Management of vaginoplasty canal complications
Bibliographic record
Abstract
PURPOSE OF REVIEW: Increasing uptake of gender affirming surgery has allowed for a wider breadth of publication examining complications associated with vaginoplasty. This review aims to provide a comprehensive overview of complications associated with vaginoplasty procedures, focusing on intraoperative, early postoperative, and delayed postoperative complications across different surgical techniques. RECENT FINDINGS: Intraoperative complications such as bleeding, injury of the rectum, urethra and prostate, and intra-abdominal injury are discussed, with insights into their incidence rates and management strategies. Early postoperative complications, including wound dehiscence, infection, and voiding dysfunction, are highlighted alongside their respective treatment approaches. Moreover, delayed postoperative complications such as neovaginal stenosis, vaginal depth reduction, vaginal prolapse, rectovaginal fistula, and urinary tract fistulas are assessed, with a focus on their etiology, incidence rates, and management options. SUMMARY: Vaginoplasty complications range from minor wound issues to severe functional problems, necessitating a nuanced understanding of their management. Patient counseling, surgical approach, and postoperative care optimization emerge as crucial strategies in mitigating the impact of complications. Standardizing complication reporting and further research are emphasized to develop evidence-based strategies for complication prevention and management in vaginoplasty procedures.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".