Reinfection Following Successful Direct-acting Antiviral Therapy for Hepatitis C Virus Infection Among People Who Inject Drugs
Bibliographic record
Abstract
BACKGROUND: The aim of this analysis was to calculate the incidence of hepatitis C virus (HCV) reinfection and associated factors among 2 clinical trials of HCV direct-acting antiviral treatment in people with recent injecting drug use or currently receiving opioid agonist therapy (OAT). METHODS: Participants who achieved an end-of-treatment response in 2 clinical trials of people with recent injecting drug use or currently receiving OAT (SIMPLIFY and D3FEAT) enrolled between March 2016 and February 2017 in 8 countries were assessed for HCV reinfection, confirmed by viral sequencing. Incidence was calculated using person-time of observation and associated factors were assessed using Cox proportional hazard models. RESULTS: Seventy-three percent of the population at risk of reinfection (n = 177; median age, 48 years; 73% male) reported ongoing injecting drug use. Total follow-up time at risk was 254 person-years (median, 1.8 years; range, 0.2-2.8 years). Eight cases of reinfection were confirmed for an incidence of 3.1/100 person-years (95% confidence interval [CI], 1.6-6.3) overall and 17.9/100 person-years (95% CI, 5.8-55.6) among those who reported sharing needles/syringes. Younger age and needle/syringe sharing were associated with HCV reinfection. CONCLUSIONS: These data demonstrate the need for ongoing monitoring and improved strategies to prevent HCV reinfection following successful treatment among people with ongoing injecting drug use to achieve HCV elimination. CLINICAL TRIALS REGISTRATION: NCT02336139 and NCT02498015.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".