Paired Recipient-based Evaluation of Survival Prediction for Deceased Donor Kidney Transplants.

Misaki Matsuura, Mohammadreza Nemati, Dulat Bekbolsynov, Stanislaw Stepkowski, Kevin S Xu

Abstract read

In one paragraph

Article in Proceedings of machine learning research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Misaki MatsuuraDepartment of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA.

Mohammadreza NematiDepartment of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA.

Dulat BekbolsynovDepartment of Medical Microbiology and Immunology, University of Toledo, Toledo, OH 43614, USA.

Stanislaw StepkowskiDepartment of Medical Microbiology and Immunology, University of Toledo, Toledo, OH 43614, USA.

Kevin S XuDepartment of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA.

Funding

Machine Learning and Network Science for Predicting Kidney Transplant Survival R01LM013311 · NLM · UNIVERSITY OF TOLEDO · PI STEPKOWSKI, STANISLAW M, XU, KEVIN SHUAI · 2019 to 2021

$830k

NLM NIH HHS R01 LM013311

6 · The paper itself

Abstract

There has been significant interest in using machine learning algorithms to predict kidney transplant outcomes, such as the number of years until a graft inevitably fails. These prediction algorithms could possibly be used for pre-transplant donor-recipient matching to identify more compatible donors and recipients and thus improve post-transplant outcomes. In this study, we explore the use of survival prediction models trained on deceased donor kidney transplant data from the Scientific Registry of Transplant Recipients (SRTR). We propose a novel paired recipient-based evaluation framework that compares graft outcomes between two recipients who received kidneys from the same deceased donor, allowing us to evaluate the

Identifiers

PMID42836069

PMCPMC13637472

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.