Reinfection incidence and risk among people treated for recent hepatitis C virus infection
Bibliographic record
Abstract
OBJECTIVE: Reinfection poses a challenge to hepatitis C virus (HCV) elimination. This analysis assessed incidence of, and factors associated with reinfection among people treated for recent HCV (duration of infection <12 months). METHODS: Participants treated for recent HCV (primary infection or reinfection) in an international randomized trial were followed at 3-monthly intervals for up to 2 years to assess for reinfection. Reinfection incidence was calculated using person-time of observation. Factors associated with HCV reinfection were assessed using Cox proportional hazards regression analysis. RESULTS: Of 222 participants treated for recent HCV, 196 (62% primary infection, 38% reinfection) were included in the cohort at risk for reinfection, of whom 87% identified as gay or bisexual men, 71% had HIV and 20% injected drugs in the month prior to enrolment. During 198 person-years of follow-up, 28 cases of HCV reinfection were identified among 27 participants, for an incidence of 14.2 per 100 person-years [95% confidence interval (CI) 9.8-20.5]. Reinfection was associated with prior HCV reinfection [adjusted hazards ratio (aHR) 2.42; 95% CI 1.08-5.38], injection drug use posttreatment (aHR 2.53; 95% CI 1.14-5.59), condomless anal intercourse with casual male partners (aHR 3.32; 95% CI 1.14-9.65) and geographic region (United Kingdom, aHR 0.21; 95% CI 0.06-0.75). Among gay and bisexual men (GBM), reinfection was also associated with sexualized drug use involving injecting posttreatment (aHR 2.97; 95% CI 1.10-8.02). CONCLUSION: High reinfection incidence following treatment for recent HCV among people with ongoing sexual and drug use risk behaviour highlights the need for posttreatment surveillance, rapid retreatment of reinfection and targeted harm reduction strategies.
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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 | 0.000 | 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".