De Novo Hepatocellular Carcinoma Among Liver Transplant Registrants in the Direct Acting Antiviral Era
Bibliographic record
Abstract
The risk of hepatocellular carcinoma (HCC) in patients with hepatitis C virus (HCV) receiving direct acting antivirals (DAAs) has been debated. This study aims to describe the incidence of HCC among patients listed for liver transplantation (LT) in the DAA era. Individuals with cirrhosis listed for LT from January 2003 to December 2015 were identified using the Scientific Registry for Transplant Recipients database. Patients with HCC at listing or HCC exception within 180 days were excluded. Patients were divided into three eras based on listing date: eras 1 (2003-2010), 2 (2011-2013), and 3 (2014-2015). Incidence rates of HCC were calculated by era and compared using incident rate ratios (IRRs). The association between HCC and listing era was evaluated using Cox regression and competing risk analyses, the latter considering death and LT as competing events. Of the 48,158 eligible wait-list registrants, 3112 (6.5%) received HCC exceptions after a median of 493 days. In 20,039 individuals with HCV, the incidence of HCC was 49% higher in era 3 versus era 1 (IRR 1.49, 95% confidence interval [CI] 1.24-1.79). In multivariate analysis, those in era 3 had a higher hazard of HCC compared with era 1 (hazard ratio 1.22, 95% CI 1.01-1.48). However, in multivariable competing risks analysis, with death and LT considered as competing events for de novo HCC, era was no longer associated with HCC (subdistribution hazard ratio 0.83, 95% CI 0.69-1.00). CONCLUSION: In this large population-based cohort of LT registrants, the incidence of HCC among HCV patients has increased in the DAA era. Competing risks analysis suggests that this may be explained by changes in rates of LT and wait-list mortality in the HCV population during this time. (Hepatology 2018; 00:000-000).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".