Getting old in the desired gender: a systematic review on aging diseases in transgender people
Bibliographic record
Abstract
INTRODUCTION: The growing demographic presence of the transgender (TGD) population has sparked an increase in clinical investigations focusing on the impacts of gender-affirming hormone therapy (GAHT) in adults with gender dysphoria. Despite this surge in studies, there remains a significant gap in the literature regarding the health status of older TGD individuals. This review aims to assess prevalent pathological conditions within the TGD population, specifically concentrating on aging-related diseases investigated to date. METHODS: A systematic search across Embase Ovid, Scopus, PubMed, Cochrane Library, and Web of Science databases was conducted to identify articles reporting on the aging process in TGD individuals. Methodological quality was evaluated using Newcastle-Ottawa Scale (NOS) scores. RESULTS: Initial database searches yielded 12,688 studies, which were refined to 18 through elimination of duplicates and title/abstract review. Following a comprehensive appraisal, nine studies were included in the systematic review. These articles, published between 2017 and 2023, involved a total of 5403 participants. The evidence indicates a noteworthy percentage of the TGD population being at risk for cardiovascular diseases, experiencing depression or disability, and demonstrating hesitancy toward major recommended screening programs. CONCLUSIONS: Limited studies on older TGD individuals highlight not only an organic risk of chronic diseases but also a cognitive/psychiatric risk that should not be underestimated. Further research is imperative to deepen our understanding of the pathophysiological mechanisms involved in the health challenges faced by older TGD 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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".