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 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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".