Directly observed therapy for the treatment of hepatitis C virus infection in current and former injection drug users
Bibliographic record
Abstract
Abstract Background and Aim: There are few studies investigating the treatment of hepatitis C virus (HCV) infection in current and former drug users. With this in mind, we sought to evaluate the antiviral efficacy of interferon alpha‐2b (IFN α‐2b) or pegylated‐interferon alpha‐2b (PEG‐IFN α‐2b) and ribavirin (RBV) in injection drug users (IDU) enrolled in a directly observed therapy (DOT) program, as measured by sustained virologic response (SVR). Methods: Viremic HCV‐infected IDU, with alanine aminotransferase (ALT) >1.5× upper limit of normal (ULN) were offered 24–48 week (based on HCV genotype) therapy with RBV (800–1200 mg/day, based on weight) along with IFN α‐2b (3 million IU thrice weekly) replaced by PEG‐IFN α‐2b (1.5 ìg/kg once weekly) as it became available. All injections were directly observed. The primary endpoint was SVR. Results: Overall, 40 patients (33 males) received IFN α‐2b (12) or PEG‐IFN α‐2b (28), 55% with HCV genotypes 2 or 3. Only 14 discontinued therapy, 5 due to toxicity, 6 due to illicit drug use and 3 did not achieve an early virologic response. In an intent‐to‐treat analysis, the overall SVR was 55% (22/40), 64% (14/22) in subjects with genotypes 2/3. There was no significant difference in response rates among those with >6 (50%) or ≤6 months (64%) drug abstinence (P = 0.51) or among those with (53%) and without (57%) intercurrent drug use (P = 0.99); however, frequent users (n = 9) had a decreased SVR (22%) when compared with occasional users (n = 10, 80%,P = 0.12). Conclusion: Treatment of HCV in current and former IDU within a multidisciplinary DOT program can be successfully undertaken, resulting in SVR similar to those in randomized controlled trials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".