Risk of <i>De Novo</i> Hepatocellular Carcinoma Following Use of Direct Acting Antiviral Medications for Treatment of Chronic Hepatitis C
Bibliographic record
Abstract
Direct-acting antivirals (DAA) are now the mainstay of treatment for patients with chronic hepatitis C virus (HCV); however, there is some controversy over whether use of DAAs for HCV, as compared with IFN-based regimens, leads to an increased risk for hepatocellular carcinoma (HCC) development. We investigated the association between use of DAAs and subsequent development of HCC in longitudinal data from patients with HCV from diverse backgrounds (various ages, ethnicities, and geographic regions) across the United States. The design was a retrospective study performed using medical and pharmacy claims from OptumLabs. HCV treatment exposure was categorized as DAA-only, DAA + IFN, any-DAA, or IFN-only. To account for confounding by indication, inverse probability of treatment weighting was performed. Cox proportional hazard models were used to calculate hazard ratios (HR) and 95% confidence intervals (CI). We identified 5,781 patients with HCV with no history of HCC at baseline. Compared with IFN-only regimen, no significant increase in HCC risk was found for use of DAA-only (HR, 1.53; 95% CI, 0.73-3.23), DAA + IFN (HR, 1.02; 95% CI, 0.51-2.06), or any-DAA (HR, 1.04; 95% CI, 0.65-1.65). When stratified by sustained virological response (SVR), we noted a higher HCC risk for DAA-only among patients who achieved SVR post-treatment (HR, 7.53; 95% CI, 1.48-38.34), but the CIs were wide, which might be due to the small sample size of the subgroups. Among those who did not achieve SVR, no association was found for use of DAA-only (HR, 0.59; 95% CI, 0.19-1.91). These findings do not provide compelling evidence for the conception that use of DAAs for HCV is associated with increased risk of HCC development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".