Gender-affirming orchiectomy with or without scrotectomy? Patients’ perspectives and surgical outcomes
Bibliographic record
Abstract
Gender-affirming orchiectomy (GAO) is a common procedure for transgender and gender diverse (TGD) individuals assigned male at birth, alleviating genital dysphoria, eliminating anti-androgen use, and serving either as a stand-alone surgery or as a step toward future vaginoplasty.1,2 An optional scrotectomy (GAO + S) removes scrotal tissue that may contribute to dysphoria or discomfort, but limits vaginoplasty techniques that rely on scrotal grafts.3 The World Professional Association for Transgender Health Standards of Care3 emphasizes aligning procedures with individual goals, yet supporting evidence remains limited. While GAO is increasingly performed in North America,4 (see also reference 6 in Supplementary References), outcomes and preferences for GAO versus GAO + S are rarely described. This study compares baseline characteristics, surgical goals, and outcomes of GAO and GAO + S, providing new data to inform shared decision-making. We conducted a retrospective single-center cohort study of patients undergoing GAO or GAO + S between November 2017 and January 2024. Exclusions included orchiectomy performed elsewhere, as part of vaginoplasty, or for indications other than gender-affirmation. Demographics, comorbidities, and gender-specific variables were abstracted from electronic medical records. Gender identity was collapsed into two categories: woman/trans-woman and non-binary/other.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".