Human Papillomavirus in the Neovagina of Transgender Women in Thailand: Prevalence, Diversity, and Associated Risk Factors
Bibliographic record
Abstract
BACKGROUND: Transgender women (TGW) face unique sexual health challenges, including neovagina complications, with limited research on sexually transmitted disease prevalence and risk factors. This study aimed to investigate the prevalence, genotype diversity, and associated risk factors of human papillomavirus (HPV) infection in the neovagina of TGW in Thailand. Cisgender women (CGW) are included to provide contextual insights. METHODS: We conducted a cross-sectional study from April to October 2023, recruiting 63 TGW from gender care clinics and community health centers, and 25 CGW from routine gynecological checkups. Samples were collected via Papanicolaou test from the neovagina of TGW and the cervix of CGW for HPV testing. Participants completed questionnaires on sociodemographic characteristics, sexual behaviors, and health-related factors. Univariate Poisson regression was used to explore associations with HPV infection. RESULTS: Among TGW, 66.7% had any HPV type, with 41.3% being high-risk and 36.5% having multiple infections. In CGW, 24% had any HPV type and 20% had high-risk HPV. About 34.9% of TGW had 9-valent HPV vaccine-preventable types. Inconsistent condom use and syphilis exposure were associated with a higher risk of any HPV infection (risk ratio [RR] of 1.59 [95% confidence interval {CI}, 1.07-2.35] and RR of 1.57 [95% CI, 1.29-1.90]) and lack of awareness linked to high-risk infection (RR, 1.84 [95% CI, 1.04-3.24]) among TGW. Conversely, CGW showed vaginal and pelvic symptoms as the only risk factor. CONCLUSION: This novel study of HPV prevalence in TGW neovaginas reveals a high burden of both high- and low-risk types, underscoring the urgent need for tailored prevention, education, vaccination, and screening.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".