Reinfection with hepatitis C virus following sustained virological response in injection drug users
Bibliographic record
Abstract
BACKGROUND AND AIM: Despite that 60-90% of injection drug users (IDUs) are infected with hepatitis C virus (HCV) infection, IDUs are often denied therapy based on concerns of reinfection following treatment. However, there are little data in this regard. We evaluated HCV re-infection following sustained virologic response (SVR) among HCV-infected IDUs having received HCV treatment in a multidisciplinary program. METHODS: Following treatment, participants were encouraged to return at follow-up intervals of 1 year and illicit drug use histories were obtained. In those with SVR, HCV RNA testing by PCR was performed to determine if relapse or reinfection occurred. RESULTS: Among 58 receiving HCV treatment between January 2002 and December 2006, 60% (35 of 58) achieved an SVR. Patients were followed for a median of 2.0 years following SVR (range, 0.4-5.0 years), with ongoing illicit and injection drug use reported in 54% (19 of 35) and 46% (16 of 35). Of the 35 with SVR, 28 remained HCV RNA negative during follow-up (80%), with four lost to follow-up and one dying of hepatocellular carcinoma and two cases of reinfection were observed (2 of 35). The rates of reinfection were 3.2 per 100 p-y (95% CI:0.4, 11.5) overall and 5.3 per 100 p-y (95% CI:0.6, 19.0) among those reporting injecting following SVR (n = 16). One of two participants with HCV re-infection spontaneously cleared virus following reinfection. CONCLUSION: The rate of reinfection following treatment for HCV infection among current and former IDUs engaged in a multidisciplinary program is low.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".