Risk Factors for Hepatitis C Virus Reinfection After Sustained Virologic Response in Patients Coinfected With HIV
Bibliographic record
Abstract
Background: Highly effective hepatitis C virus (HCV) therapies have spurred a scale-up of treatment to populations at greater risk of reinfection after sustained virologic response (SVR). Reinfection may be higher in HIV-HCV coinfection, but prior studies have considered small selected populations. We assessed risk factors for reinfection after SVR in a representative cohort of Canadian coinfected patients in clinical care. Methods: All patients achieving SVR after HCV treatment were followed with HCV RNA measurements every 6 months in a prospective cohort study. We used Bayesian Cox regression to estimate reinfection rates according to patient reported injection drug use (IDU) and sexual activity among men who have sex with men (MSM). Results: Of 497 patients treated for HCV, 257 achieved SVR and had at least 1 subsequent RNA measurement. During 589 person-years of follow-up (PYFU) after SVR, 18 (7%) became HCV RNA positive. The adjusted reinfection rate (per 1000 PYFU) in the first year after SVR was highest in those who reported high-frequency IDU (58; 95% credible interval [CrI], 18-134) followed by MSM reporting high-risk sexual activity (26; 95% CrI, 6-66) and low-frequency IDU (22; 95% CrI, 4-68). The rate in low-risk MSM (16; 95% CrI, 4-38) was similar to that in reference patients (10; 95% CrI, 4-20). Reinfection rates did not diminish with time. Conclusions: HCV reinfection rates varied according to risk. Measures are needed to reduce risk behaviors and increase monitoring in high-risk IDU and MSM if HCV elimination targets are to be realized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.034 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".