Retrospective Analysis of Recurrent HCV Viremia in High-risk HIV Co-infected People Who Inject Drugs (PWID)
Bibliographic record
Abstract
Background: People who inject drugs (PWID) are overrepresented in the HCV-infected community. Past Canadian guidelines do not advocate HCV treatment of HIV co-infected PWID, fearing reduced AA¸cAcy and recurrent viremia AAAr successful treatment, due to ongoing risk behaviours. These factors may be more prominent among those co-infected with HIV. Methods: A Retrospective chart review analysis was performed to identify HIV/HCV co-infected individuals who actively injected drugs within 6 months preceding or during HCV treatment. Information regarding AEAAEAnAE cAArAcAEArAAEAEcAEI risk behaviours, HCV treatment, and virologic follow-up post-treatment was collected. Results: We identified 45 HIV/HCV co-infected PWID (mean age 51.9 years, 6.7% female, 57.8% on opiatesubstitution therapy, 66.7% genotype 1, 82.2% treatment naive, 73.3% on interferon-based therapies, and 1.52 person years of follow-up/subject). Following successful HCV therapy, 3 cases of HCV recurrent viremia were identified Conclusion: HCV AnA¨AcAEA½n can be successfully treated in high-risk HIV co-infected individuals. In our unique AEAAE«nAI few cases of recurrent viremia were identified in mediumterm follow-up.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".