Reduction in liver transplant wait‐listing in the era of direct‐acting antiviral therapy
Bibliographic record
Abstract
Direct-acting antiviral (DAA) therapy, recently approved for patients with decompensated cirrhosis (DC) secondary to hepatitis C virus (HCV), is associated with improved hepatic function. We analyzed trends in liver transplant (LT) wait-listing (WL) to explore potential impact of effective medical therapy on WL registration. This is a cohort study using the Scientific Registry of Transplant Recipients database from 2003 to 2015. A total of 47,591 adults wait-listed for LT from HCV, hepatitis B virus (HBV), and nonalcoholic steatohepatitis (NASH) were identified. LT indication was defined as DC if the Model for End-Stage Liver Disease (MELD) at WL was ≥15 or hepatocellular carcinoma (HCC). Era of listing was divided into interferon (IFN; 2003-2010), protease inhibitor (PI; 2011-2013), and direct-acting antiviral (DAA; 2014-2015). Annual standardized incidence rates of WL were analyzed using Poisson regression. Adjusted incidences of LT WL for DC in HCV patients decreased by 5% in the PI era (P = 0.004) and 32% in the DAA era (P < 0.001) compared to the IFN era. Listing for DC in HBV also decreased in the PI (-17%; P = 0.002) and DAA eras (-24%; P < 0.001). Conversely, WL for DC in NASH increased by 41% in the PI era (P < 0.001) and 81% in the DAA era (P < 0.001). WL for HCC in both the HCV and NASH populations increased in both the PI and DAA eras (P < 0.001 for all) whereas HCC WL in HBV remained stable (P > 0.05 for all). CONCLUSION: The rate of LT WL for HCV complicated by DC has decreased by over 30% in the era of DAA therapy. Further reductions in WL are anticipated with increased testing, linkage to care, and access to DAA therapy. (Hepatology 2017;65:804-812).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".