Incidence and risk factors for recurrent sexually transmitted infections among MSM on HIV pre-exposure prophylaxis
Bibliographic record
Abstract
OBJECTIVE: High rates of sexually transmitted infections (STIs) have been reported among pre-exposure prophylaxis (PrEP) users. We wished to assess the incidence and risk factors for recurrent STIs. DESIGN: The ANRS IPERGAY trial was a prospective study investigating PrEP among MSM and transgender women in outpatient clinics in France and Canada. In all, 429 participants were enrolled, offered up to 4 years of PrEP and screened for bacterial STIs (syphilis, chlamydia and gonorrhea) at baseline and every 6 months. METHODS: STIs incidence was calculated yearly. Cox proportional hazards model regression was used to explore associations between participants characteristics at baseline and recurrent STI during follow-up. RESULTS: Over a median follow-up of 23 months, bacterial STI incidence was 75, 33, 13, 32 and 30 per 100 person-years for all STIs, rectal STIs, syphilis, gonorrhea and chlamydia, respectively. STI incidence significantly increased from the first year to the fourth year of the study (55 vs. 90 per 100 person-years, P < 0.001). During the study period, 167 participants (39%) presented with more than one bacterial STIs which accounted for 86% of all STIs. Baseline risk factors associated with recurrent STIs in a multivariate analysis were an STI at baseline [hazards ratio: 1.48 (95% confidence interval (CI): 1.06-2.07), P = 0.02], more than eight sexual partners in prior 2 months [hazards ratio: 1.72 (95% CI: 1.21-2.43), P = 0.002] and the use of gamma-hydroxybutyrate [hazards ratio: 1.66 (95% CI: 1.16-2.38), P = 0.005]. CONCLUSION: STI incidence was high and increased over time. Most STIs were concentrated in a high-risk group that should be targeted for future interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".