A212 INCIDENCE OF HEPATITIS C VIRUS INFECTIONS AMONG USERS OF HUMAN IMMUNODEFICIENCY VIRUS PRE-EXPOSURE PROPHYLAXIS
Bibliographic record
Abstract
Abstract Background Sexual transmission of hepatitis C virus (HCV) is well-documented among HIV-uninfected individuals. The use of HIV pre-exposure prophylaxis (PrEP) may lead to increased engagement in activities that facilitate the transmission of sexually transmitted infections (STI) and possibly HCV among PrEP users. Aims To assess the incidence of Hepatitis C Virus Infections among HIV negative pre-exposure prophylaxis (PrEP) users Methods Between 2012 and 2019, the incidence of HCV and bacterial STIs were calculated among HIV-negative patients receiving PrEP at the University Health Network HIV Prevention Clinic. Mucosal, anal and blood samples were taken to test for HIV, syphilis, and anti-HCV antibodies. Results Among 344 HIV-uninfected patients receiving PrEP, 86% were men having sex with men (MSM). Five individuals were HCV-antibody positive at the time of PrEP initiation. Serological and virological follow-up was available for 109 HCV-negative individuals over 282 patient-years (PY). Two new infections were recorded, yielding an incidence of primary HCV infection of 0.7/100 PY. In contrast with HCV, the incidence rates of chlamydia, gonorrhea, and syphilis were 49.2/100 PY, 36.3/100 PY, and 5.2/100 PY, respectively. Both individuals with new HCV diagnoses reported being MSM with a history of unprotected intercourse and one also reported recreational drug use. Both individuals were asymptomatic at the time of diagnosis and were detected by routine laboratory monitoring. Conclusions The low incidence of HCV infections despite significantly higher rates of other STIs suggests that sexual transmission of HCV is uncommon in HIV-negative MSM PrEP users. Performing routine risk-based HCV surveillance among PrEP users should be evaluated. The high incidence of STIs in this population indicates a vital role for periodic STI monitoring in those receiving PrEP. Funding Agencies vircan
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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 | 0.002 |
| 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 | 0.001 |
| 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".