Incidence of Testicular Cancer in Transfeminine Patients following Vaginoplasty with Orchidectomy
Bibliographic record
Abstract
Background: Little is known about the prevalence of testicular cancer in the transfeminine population. Only six cases have been reported in the literature. This case series reports six additional cases of various testicular cancers found in transfeminine patients who underwent vaginoplasty with orchidectomy in our institution. Methods: In our institution, all specimens are routinely sent to pathology following vaginoplasty with orchidectomy. This permitted the identification of all positive cases of testicular cancer. A chart review was conducted to retrieve patient demographics, duration of hormonotherapy, type of neoplasm, the context of its discovery, and cancer follow-up. Results: A total of 2555 patients underwent vaginoplasty with orchidectomy between January 2016 and January 2021. All specimens were sent to pathology for analysis. A total of six (0.23% of patients) specimens revealed malignant lesions. Conclusions: Increased societal awareness toward the transgender population encourages recourse to gender-affirming procedures. Little is known about the incidence of testicular cancer in the transfeminine population. In total, 0.23% of patients in our cohort presented with positive pathology findings indicative of testicular cancer. All cancers were found to be only locally invasive, and all patients were successfully treated. We therefore encourage routine pathology examination for all specimens following vaginoplasty with orchidectomy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".