Staying hepatitis C negative: A systematic review and meta‐analysis of cure and reinfection in people who inject drugs
Bibliographic record
Abstract
BACKGROUND AND AIMS: Direct-acting antivirals (DAAs) are highly effective in treating hepatitis C. However, there is concern that cure rates may be lower, and reinfection rates higher, among people who inject drugs. We conducted a systematic review of treatment outcomes achieved with DAAs in people who inject drugs (PWID). METHODS: A search strategy was used to identify studies that reported sustained viral response (SVR), treatment discontinuation, adherence or reinfection in recent PWID and/or opioid substitution therapy (OST) recipients. Study quality was assessed using the Newcastle-Ottawa Scale. Meta-analysis of proportions was used to estimate pooled SVR and treatment discontinuation rates. The pooled relative risk of achieving SVR and pooled reinfection rate were calculated using generalized mixed effects linear models. RESULTS: The search identified 8075 references; 26 were eligible for inclusion. The pooled SVR for recent PWID was 88% (95% CI, 83%-92%) and 91% (95% CI 88%-95%) for OST recipients. The relative risk of achieving SVR for recent PWID compared to non-recent PWID was 0.99 (95% CI, 0.94-1.06). The pooled treatment discontinuation was 2% (95% CI, 1%-4%) for both recent PWID and OST recipients. Amongst recent PWID, the pooled incidence of reinfection was 1.94 per 100 person years (95% CI, 0.87-4.32). In OST recipients, the incidence of reinfection was 0.55 per 100 person years (95% CI, 0.17-1.76). CONCLUSIONS: Treatment outcomes were similar in recent PWID compared to non-PWID treated with DAAs. People who report recent injecting or OST recipients should not be excluded from hepatitis C treatment.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".