Stroke in Male-to-Female Transgenders: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The effect of hormonal therapy has been extensively studied in women. However, similar data on male-to-female (MTF) transgenders, another important population that receives hormonal therapy is lacking. Existing studies in MTF transgenders are skewed toward mental health and health-harming behaviors while few have focused on chronic health conditions. Our study aims to review the existing data on stroke in MTF transgenders and perform a quantitative analysis on the frequency of this condition in this special population. METHODS: PubMed, Cochrane, Scopus, Embase, ClinicalTrials.gov, and Web of Science were systematically searched for studies that reported data on the occurrence of cerebrovascular diseases in MTF transgenders. We reported the hormonal regimens, clinical characteristics, and outcomes of stroke in MTF transgenders. A meta-analysis of proportions was performed by the random-effects model to compute for the frequency of cerebrovascular events in MTF transgenders. RESULTS: Fourteen studies were included in the qualitative analysis while five studies were included in the quantitative analysis. A total of 109 MTF transgenders (Mean 14; range 1-53) suffered a cerebrovascular event. Random-effect modeling analysis showed an overall estimated frequency of 2% for cerebrovascular events in transgenders with a moderate degree of heterogeneity (I2 = 62%). CONCLUSION: Hormonal therapy in MTF transgenders may confer cardiovascular risks in this population. However, more population-based studies that include clinical characteristics and outcomes of chronic health diseases in MTF transgenders are warranted. Such studies may be crucial in directing future guidelines on the health care and management of MTF transgenders.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".