Sexually transmitted infections incidence in young Thai men who have sex with men and transgender women using HIV pre-exposure prophylaxis
Bibliographic record
Abstract
Background Sexually transmitted infections are a major public health issue worldwide. HIV pre-exposure prophylaxis (PrEP) use among youth may be associated with increased incidence of sexually transmitted infections (STIs). Objectives To measure the prevalence and incidence of STIs among young men who have sex with men (YMSM) and young transgender women (YTGW) using PrEP. Methods A prospective cohort of 15- to 19-year-old YMSM and YTGW with HIV risk defined as inconsistent condom use and/or multiple sex partners were enrolled. Participants were provided daily oral tenofovir disoproxil fumarate/emtricitabine. STI screening was done at baseline and month 6 for syphilis, urine, and anal swab nucleic acid amplification testing for C. trachomatis (CT) and N. gonorrheaoe (NG). Results From March 2018 to June 2019, 200 adolescents (147 MSM and 53 TGW) with a median (IQR) age of 18 years (17–19) were enrolled. STI prevalence was 22.5% (95% CI 16.7–28.3). STI incidence was 25.2 per 100 person–years (95% CI 14.7, 40.3). Factors associated with STI incidence were self-reported >2 sex partners in the past month (unadjusted rate ratio [uRR] 4.6, 95% CI 1.0, 20.6), and moderate PrEP adherence (uRR 7.3, 95% CI 1.6, 32.6). Conclusions STI incidence in YMSM and YTGW PrEP users was high at approximately one in five. Regular screening and treatment of STIs should be implemented in youth HIV prevention packages.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".