HCV reinfection rates after cure or spontaneous clearance among HIV‐infected and uninfected men who have sex with men
Bibliographic record
Abstract
BACKGROUND & AIMS: Hepatitis C virus (HCV) reinfection among high-risk groups threatens HCV elimination goals. We assessed HCV reinfection rates among men who have sex with men (MSM) in British Columbia (BC), Canada. METHODS: We used data from the BC Hepatitis Testers Cohort, which includes nearly 1.7 million individuals tested for HCV or HIV in BC. MSM who had either achieved sustained virologic response (SVR) after successful HCV treatment, or spontaneous clearance (SC) and had ≥1 subsequent HCV RNA measurement, were followed from the date of SVR or SC until the earliest of reinfection, death, or last HCV RNA measurement. Predictors of reinfection were identified by Cox proportional modelling. The earliest study start date was 6 November 1997 and latest end date was 13 April 2018. RESULTS: Of 1349 HCV-positive MSM who met the inclusion criteria, 493 had SC while 856 achieved SVR. 349 (25.65%) had HIV coinfection. We identified 98 reinfections during 5203 person-years (PYs) yielding a reinfection rate of 1.88/100PYs. The reinfection rate among SC (2.74/100PYs) was more than twice that of those with SVR (1.03/100 PYs). Problematic alcohol use (aHR 1.73, 95% CI 1.003-2.92), injection drug use (aHR 2.60, 95% CI 1.57-4.29) and HIV coinfection (aHR 2.04, 95% CI 1.29-3.23) were associated with increased risk of HCV reinfection. Mental health counselling history (aHR 0.24, 95% CI 0.13-0.46) was associated with reduced HCV reinfection risk. CONCLUSIONS: There is the need to engage MSM in harm reduction and prevention services following treatment to reduce reinfection risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".