Venous Thrombotic Risk in Transgender Women Undergoing Estrogen Therapy: A Systematic Review and Metaanalysis
Bibliographic record
Abstract
Abstract BACKGROUND Transgender women are female individuals who were recorded men at birth based on natal sex. Supporting a person's gender identity improves their psychological health, and gender-affirming hormones reduce gender dysphoria and benefit mental health. For transgender women, estrogen administration has clinically significant benefits. Previous reviews have reported conflicting literature on the thrombotic risk of estrogen therapy in transgender women and have highlighted the need for more high-quality research. CONTENT To help address the gap in understanding thrombotic risk in transgender women receiving estrogen therapy, we performed a systematic literature review and metaanalysis. Two evaluators independently assessed quality using the Ottawa Scale for Cohort Studies. The Poisson normal model was used to estimate the study-specific incidence rates and the pooled incidence rate. Heterogeneity was measured using Higgins I2 statistic. The overall estimate of the incidence rate was 2.3 per 1000 person-years (95% CI, 0.8–6.9). The heterogeneity was significant (I2 = 74%; P = 0.0039). SUMMARY Our study estimated the incidence rate of venous thromboembolism in transgender women prescribed estrogen to be 2.3 per 1000 person-years, but because of heterogeneity this estimate cannot be reliably applied to transgender women as a group. There are insufficient data in the literature to partition by subgroup for subgroup prohibiting the analysis to control for tobacco use, age, and obesity, which is a major limitation. Additional studies of current estrogen formulations, modes of administration, and combination therapies, as well as studies in the aging transgender population, are needed to confirm thrombotic risk and clarify optimal therapy regimens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".