Development and validation of a machine-learning model to reduce futile procurements in donations after circulatory death in liver transplantation in the USA: a multicentre study

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Publication Details

Journal
The Lancet Digital Health
Published
2025-10-01
DOI
https://doi.org/10.1016/j.landig.2025.100918
Citations
3
Primary Topic
Organ Transplantation Techniques and Outcomes
Type
article
Field-Weighted Citation Impact
6.02
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article

Development and validation of a machine-learning model to reduce futile procurements in donations after circulatory death in liver transplantation in the USA: a multicentre study

Toshihiro Nakayama, Jenny H. Pan, Yuki Bekki, K. Bozhilov et al.
3 citations
The Lancet Digital Health
Organ Transplantation Techniques and Outcomes
6.02
article

Development and validation of a machine-learning model to reduce futile procurements in donations after circulatory death in liver transplantation in the USA: a multicentre study

Toshihiro Nakayama, Jenny H. Pan, Yuki Bekki, K. Bozhilov, Yuzuru Sambommatsu, Kazunari Sasaki, Daiki Soma, Chase J. Wehrle, Yutaka Endo, Koji Tomiyama, Daniel J. Firl, Masaru Kubota, Masato Fujiki, Magdy Attia, Marc L. Melcher, Jiro Kusakabe, Rintaro Yanagawa, Kazuhiro Iwadoh
article en
3 citations

Abstract

No abstract available for this paper.
The Lancet Digital HealthVol. 7(10)
Cleveland Clinic (US), Duke University (US), Virginia Commonwealth University (US), Kyoto University (JP), Columbia University Irving Medical Center (US), University of Rochester Medical Center (US), University of Florida (US), Andover Eye Associates (US), Stanford Medicine (US), Mita Hospital (JP), Florida College (US), Stanford University (US)
Openalex Percentile: Top 3%
Organ Transplantation Techniques and Outcomes
6.02
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