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The United Network for Organ Sharing recently altered current liver allocation with the goal of decreasing Model for End-Stage Liver Disease (MELD) variance at transplant. Concerns over these and further planned revisions to policy include predicted decrease in total transplants, increased flying and logistical complexity, adverse impact on areas with poor quality health care, and minimal effect on high MELD donor service areas. To address these issues, we describe general approaches to equalize critical transplant metrics among regions and determine how they alter MELD variance at transplant and organ supply to underserved communities. We show an allocation system that increases minimum MELD for local allocation or preferentially directs organs into areas of need decreases MELD variance. Both models have minimal adverse effects on flying and total transplants, and do not disproportionately disadvantage already underserved communities. When combined together, these approaches decrease MELD variance by 28%, more than the recently adopted proposal. These models can be adapted for any measure of variance, can be combined with other proposals, and can be configured to automatically adjust to changes in disease incidence as is occurring with hepatitis C and nonalcoholic fatty liver disease.
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http://dx.doi.org/10.1111/ajt.14889 | DOI Listing |
Data Brief
August 2025
Multidisciplinary Action Research Lab, Department of Computer Science and Engineering, Daffodil International University, Birulia, Dhaka 1216, Bangladesh.
There are thousands of ethnic groups in the world contributing to the rich linguistic and cultural diversity of people. However, in digital resources and research, the majority of these languages, including more than 30 ethnic languages spoken in Bangladesh remain severely underrepresented. There is little to no work addressing the preservation, translation, or computational processing of these languages, despite their unique linguistic structures and speaker population.
View Article and Find Full Text PDFHepatol Commun
April 2025
Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, Michigan, USA.
Background: Hepatic decompensation carries profound implications for patient quality of life and risk of mortality. We lack comparative data on how noninvasive tools perform in risk stratification for those with compensated cirrhosis. We performed a systematic review to assess the performance of laboratory and transient elastography-based models for predicting hepatic decompensation in patients with compensated cirrhosis.
View Article and Find Full Text PDFWorld J Hepatol
January 2025
Faculty of Medicine, Mansoura University, Mansoura 35511, Egypt.
Background: Chronic liver disease is a growing global health problem, leading to hepatic decompensation characterized by an array of clinical and biochemical complications. Several scoring systems have been introduced in assessing the severity of hepatic decompensation with the most frequent ones are Child-Pugh score, model of end-stage liver disease (MELD) score, and MELD-Na score. Anemia is frequently observed in cirrhotic patients and is linked to worsened clinical outcomes.
View Article and Find Full Text PDFEpilepsia
April 2025
Department of Neuroradiology, University Hospital Bonn, Bonn, Germany.
Objective: Focal cortical dysplasia (FCD) is a common cause of drug-resistant focal epilepsy but can be challenging to detect visually on magnetic resonance imaging. Three artificial intelligence models for automated FCD detection are publicly available (MAP18, deepFCD, MELD) but have only been compared on single-center data. Our first objective is to compare them on independent multicenter test data.
View Article and Find Full Text PDFEur J Surg Oncol
July 2025
Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.
Introduction: No instruments are available to predict preoperatively the risk of posthepatectomy liver failure (PHLF) in HCC patients. The aim was to predict the occurrence of PHLF preoperatively by radiomics and clinical data through machine-learning algorithms.
Materials And Methods: Clinical data and 3-phases CT scans were retrospectively collected among 13 Italian centres between 2008 and 2022.