Current Approaches to HCV Infection in Current and Former Injection Drug Users
Bibliographic record
Abstract
Injection drug use (IDU) accounts for 75% of incident cases of hepatitis C virus (HCV) infection in the developed world. Of those infected with HCV, up to 80% will go on to develop chronic disease. Intervention with effective treatment in eligible subjects will limit the impact of the long-term consequences of infection. The use of combination therapy with pegylated interferon and ribavirin may lead to a cure in up to 80% of treated individuals who carry genotype 2 or 3 isolates. Such individuals account for up to 45% of certain cohorts, such as in the inner city of Vancouver. Historically, many IDUs have not received treatment for HCV infection even if it were medically indicated. Recent data (including our own) suggest that, in the right context, response rates similar to those reported in clinical trials of HCV therapy can be achieved in IDUs, even with ongoing drug use. This is all the more important given that prior infection may protect against re-infection even in the presence of ongoing risk behaviors for HCV transmission. The keys to a successful program appear to be appropriate patient selection as well as the delivery of care within an appropriate setting, preferably with a multidisciplinary team in a way that addresses the issue of addiction and other conditions simultaneously. The development of such programs may be quite complex, but the ultimate benefit (for the treated population and for society as a whole) is certainly worth the effort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".