Evidence map›Paper›PMID 40976641›Full record

ArticleJMIR AI2025

Examining Transparency in Kidney Transplant Recipient Selection Criteria: Nationwide Cross-Sectional Study.

Belen Rivera, Stalin Canizares, Gabriel Cojuc-Konigsberg, Olena Holub, Alex Nakonechnyi, Ritah R Chumdermpadetsuk, Keren Ladin, Devin E Eckhoff, Rebecca Allen, Aditya Pawar

Abstract read
In one paragraph

Article in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Belen RiveraBeth Israel Deaconess Medical Center, Boston, MA, United States.ORCID https://orcid.org/0009-0007-4126-743X
Stalin CanizaresBeth Israel Deaconess Medical Center, Boston, MA, United States.ORCID https://orcid.org/0000-0003-4706-862X
Gabriel Cojuc-KonigsbergBeth Israel Deaconess Medical Center, Boston, MA, United States.ORCID https://orcid.org/0000-0003-3463-6309
Olena HolubDepartment of Computer Science and Mathematics, Mount St Joseph University, Cincinnati, OH, United States.ORCID https://orcid.org/0009-0003-7848-7488
Alex NakonechnyiDepartment of Computer Science and Mathematics, Mount St Joseph University, Cincinnati, OH, United States.ORCID https://orcid.org/0000-0002-1756-2136
Ritah R ChumdermpadetsukBeth Israel Deaconess Medical Center, Boston, MA, United States.ORCID https://orcid.org/0000-0002-1909-7741
Keren LadinDepartment of Community Health, Research on Aging, Ethics, and Community Health (REACH Lab), Tufts University, Boston, MA, United States.ORCID https://orcid.org/0000-0002-4310-8260
Devin E EckhoffBeth Israel Deaconess Medical Center, Boston, MA, United States.ORCID https://orcid.org/0000-0003-2621-1431
Rebecca AllenDepartment of Computer Science and Mathematics, Mount St Joseph University, Cincinnati, OH, United States.ORCID https://orcid.org/0000-0003-0044-2047
Aditya PawarBeth Israel Deaconess Medical Center, Boston, MA, United States.ORCID https://orcid.org/0000-0002-9178-4386

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChoosing a transplant program impacts a patient's likelihood of receiving a kidney transplant. Most patients are unaware of the factors influencing their candidacy. As patients increasingly rely on online resources for health care decisions, this study quantifies the available online patient-level information on kidney transplant recipient (KTR) selection criteria across US kidney transplant centers.

objectiveWe aimed to use natural language processing and a large language model to quantify the available online patient-level information regarding the guideline-recommended KTR selection criteria reported by US transplant centers.

methodsA cross-sectional study using natural language processing and a large language model was conducted to review the websites of US kidney transplant centers from June to August 2024. Links were explored up to 3 levels deep, and information on 31 guideline-recommended KTR selection criteria was collected from each transplant center.

resultsA total of 255 US kidney transplant centers were analyzed, comprising 10,508 web pages and 9,113,753 words. Among the kidney transplant guideline-recommended KTR selection criteria, only 2.6% (206/7905) of the information was present on the transplant center web pages. Socioeconomic and behavioral criteria were mentioned more than those related to the patient's medical conditions and comorbidities. Of the 31 criteria, finances and health insurance was the most frequently mentioned, appearing in 25.5% (65/255) of the transplant centers. Other socioeconomic and behavioral criteria, such as family and social support systems, adherence, and psychosocial assessment, were addressed in less than 4% (9/255) of the transplant centers. No information was found on any web page for 45.2% (14/31) of the KTR selection criteria. Geographically, disparities in reporting were observed, with the South Atlantic division showing the highest number of distinct criteria, while New England had the fewest.

conclusionsMost transplant center websites do not disclose patient-level KTR selection criteria online. The lack of transparency in the evaluation and listing process for kidney transplantation may limit patients in choosing their most suitable transplant center and successfully receiving a kidney transplant.

Indexed as

AIartificial intelligencekidney transplantkidney transplant recipientlarge language modelnatural language processingselection criteria

Identifiers

PMID40976641
PMCPMC12627972

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Registered trials

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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.