Antiviral Treatment and Response are Associated With Lower Risk of Dementia Among Hepatitis C Virus-Infected Patients
Bibliographic record
Abstract
OBJECTIVE: Eradication of hepatitis C virus (HCV) infection has been linked with improvement in neurocognitive function, but few studies have evaluated the effect of antiviral treatment/ response on risk of dementia. Using data from the Chronic Hepatitis Cohort Study (CHeCS), we investigated how antiviral therapy impacts the risk of developing dementia among patients with HCV. METHODS: A total of 17,485 HCV patients were followed until incidence of dementia, death, or last follow-up. We used an extended landmark modeling approach, which included time-varying covariates and propensity score justification for treatment selection bias, as well as generalized estimating equations (GEE) with a link function as multinominal distribution for a discrete time-to-event data. Death was considered a competing risk. RESULTS: After 15 years of follow-up, 342 patients were diagnosed with incident dementia. Patients who achieved sustained virological response (SVR) had significantly decreased risk of dementia compared to untreated patients, with hazard ratios (HRs) of 0.32 (95% CI 0.22-0.46) among patients who received direct-acting antiviral (DAA) treatment and 0.41 (95% CI 0.26-0.60) for interferon-based (IFN) treatment. Risk reduction remained even when patients failed antiviral treatment (HR 0.38, 95% CI 0.38-0.51). Patients with cirrhosis, Black/African American patients, and those without private insurance were at significantly higher risk of dementia. CONCLUSION: Antiviral treatment independently reduced the risk of dementia among HCV patients, regardless of cirrhosis. Our findings support the importance of initiation antiviral therapy in chronic HCV-infected patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".