Unsuccessful Direct Acting Antiviral Hepatitis C Treatment Among People With <scp>HIV</scp>: Findings From an International Cohort
Bibliographic record
Abstract
BACKGROUND: Historically, hepatitis C virus (HCV) was difficult to treat among people with HIV. However, treatment with direct-acting antivirals (DAAs) results in 90%-95% of people being cured. There is a need to understand why a proportion of people are not cured. We aimed to examine characteristics that may indicate an increased probability of unsuccessful DAA HCV treatment. METHODS: Data were from the International Collaboration on Hepatitis C Elimination in HIV Cohorts. People who commenced DAA HCV treatment between 2014 and 2019 were included. Unsuccessful treatment was defined as a positive HCV RNA test at a person's first RNA test at least 4 weeks (SVR4+) following the end of treatment. Multivariable mixed-effects logistic regression was used to examine characteristics associated with unsuccessful treatment. RESULTS: , cell counts < 200 (aOR 1.81, 95%CI 1.00-3.29) and between 200 and 349 (aOR 1.95, 95%CI 1.30-2.93) were associated with increased odds of unsuccessful treatment. Among 1921 people with data on injection drug use in the 12 months prior to treatment, there was some evidence that recent injection drug use was associated with increased odds of unsuccessful treatment; however, this was not statistically significant (aOR 1.67, 95%CI 0.99-2.82). CONCLUSIONS: The overwhelming majority of people were successfully treated for HCV. Overall, 5% of those with an SVR4+ test were unsuccessfully treated; this was more likely among people with evidence of immunodeficiency and those who reported recently injecting drugs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".