Hepatitis C Virus Reinfection Following Treatment Among People Who Use Drugs
Bibliographic record
Abstract
Most new cases of hepatitis C virus (HCV) infections in the developed world are associated with injection drug use. However, treatment for people who inject drugs (PWID) is controversial, as successful treatment risks being followed by new infection. Reinfection after sustained virologic response has been reported, but is the risk so great that treatment should be withheld from this large HCV population? Preliminary evidence suggests that the reinfection incidence is low, but studies to date have been limited by small sample size and few cases of reinfection. In this review, we assess data from studies among PWID of HCV reinfection following treatment to give a reasonable estimate on how frequently reinfection appears and try to characterize those most at risk, The observation that spontaneous clearance of HCV reinfection following treatment occurs is suggestive of a partial protective immunity against persistent infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".