Perinatal outcomes of transgender individuals with prior testosterone use: A systematic review
Bibliographic record
Abstract
Introduction Testosterone is a mainstay of gender affirming treatment for transgender men and masculine gender diverse populations. There is limited data regarding the use of testosterone in the peripartum period, and it is unknown if testosterone use impacts perinatal outcomes.Methods To assess perinatal outcomes among transgender and gender diverse (TGD) birthing persons with prior testosterone use, a comprehensive literature search identified 304 studies. Title and abstract screening were performed by two reviewers. Studies were eligible if they reported perinatal outcomes in TGD individuals with prior testosterone use and included at least three participants. Seventeen studies underwent full-text review. Data extraction and quality assessment were performed independently by two reviewers using a standardized form and the Newcastle-Ottawa Scale.Results Three studies (N = 262 pregnancies among TGD individuals) were included in the systematic review; two were included in the meta-analysis. Meta-analysis of mode of delivery showed no statistically significant difference in vaginal delivery rates between those with and without prior testosterone exposure. Perinatal outcomes (hypertension, preterm labor, placental abruption, anemia, gestational diabetes) were variably reported across studies. Study quality ranged from poor to fair due to limitations in study design, outcome measurement, and sample size.Conclusion Limited evidence suggests that prior testosterone use among TGD individuals is not clearly associated with worse perinatal outcomes. However, data are sparse. Clinicians should be aware that TGD individuals may experience pregnancy following testosterone use. Further research is needed to clarify perinatal risks and guide clinical counseling on testosterone use/cessation timing, delivery planning, and postpartum testosterone resumption.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".