The Impact of Direct-acting Antivirals on Overall Mortality and Tumoral Recurrence in Patients With Hepatocellular Carcinoma Listed for Liver Transplantation: An International Multicenter Study
Bibliographic record
Abstract
BACKGROUND: There is a lack of data on the use of direct-acting antivirals (DAA) on the risk of death and tumoral recurrence in patients with hepatitis C virus (HCV) and hepatocellular carcinoma (HCC) listed for liver transplantation (LT). We aimed to assess the impact of antiviral treatment on mortality and HCC recurrence patients with HCC-HCV. METHODS: This was a retrospective multicenter study of patients with HCC-HCV listed for LT from 2005 to 2015. Patients were divided according to the antiviral treatment received after HCC diagnosis: DAA, interferon (IFN), or no antiviral. Intention-to-treat overall survival and HCC recurrence incidence were compared by the Kaplan-Meier method. Multivariable regression analysis was performed to identify risk factors for outcomes. RESULTS: A total of 1012 HCV-HCC patients were listed for LT during the study period. The median follow-up was 4.0 (interquartile range = 2.3-6.7) years. Mortality was 5.6 (95% confidence interval [CI], 4.3-7.2), 13.1 (95% CI, 11.0-15.7), and 6.2 (95% CI, 5.4-7.2) deaths per 100 person-year among patients treated with DAA, IFN, and antiviral naïve, respectively (P < 0.001). Of the 875 HCV-HCC transplant recipients, the 5-year recurrence-free survival was 93.4%, 84.8%, 73.9% for the pre-LT DAA, pre-LT IFN, and antiviral naïve groups, respectively (P < 0.001). After multivariable regression, the use of pre-LT DAA was not associated to risk of recurrence (hazard ratio = 0.44 [95% CI, 0.19-1.00]). Post-LT DAA was not related to increased risk of recurrence (hazard ratio = 0.62 [95% CI, 0.33-1.16]). CONCLUSIONS: In this multicenter intent-to-treat study, DAA therapy was not found to be a risk factor for mortality or HCC recurrence after adjusting for potential confounders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".