Rethinking prostate cancer screening in transgender women
Bibliographic record
Abstract
INTRODUCTION: Over the years, the number of people openly identifying as transgender has steadily increased, leading to a greater need for transgender-specific healthcare information. Among transgender women (TW), there remains a risk of prostate cancer since the prostate is not removed during gender-affirming hormone therapy (GAHT) or surgery; however, there is limited knowledge about prostate cancer screening in the transgender population. Although there are few reported cases of prostate cancer screening or prostate-specific antigen (PSA) testing in TW, the impact of hormone therapies on PSA levels is well-documented. Notably, GAHT for TW and hormone therapy for treating prostate cancer share similarities. Drawing on these similarities, we aimed to develop guidelines for baseline PSA levels and prostate cancer screening in TW. METHODS: Through a systematic review, we examined the existing PubMed publications on PSA levels and prostate cancer screening in TW, as well as expected PSA levels in patients with prostate cancer undergoing hormone therapies. We also investigated other aspects considered for the diagnosis of prostate cancer. Given the limited research on TW, we also included relevant case studies. These publications and case reports were reviewed and analyzed to create a comprehensive overview of expected baseline PSA levels and prostate cancer screening guidelines for TW. RESULTS: Currently, there are no established guidelines for PSA or prostate cancer screening in TW; however, these case studies indicated a range of PSA values from 3.3 to <100 ng/ml. Existing literature on expected PSA levels in prostate cancer patients undergoing hormone therapy shows a reduction in PSA of over 50% post-therapy. This evidence suggests that PSA values in TW presenting with prostate cancer may be lower than those observed in cisgender males with the disease. CONCLUSIONS: While TW exhibit lower prostate cancer incidence compared to cisgender men, the impact of hormone therapy on PSA levels presents significant challenges for screening and diagnosis. The parallels between PSA level reductions in TW and cisgender men undergoing estrogen therapy highlight the need for revised screening protocols. Addressing these challenges through targeted research and personalized care approaches will be vital for improving prostate cancer management in transgender individuals.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".