ArticleNPJ digital medicine2025
A large language model-based approach to quantifying the effects of social determinants in liver transplant decisions.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
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Who cites it
5 citing papers in PubMed.
- Leveraging Natural Language Processing to Identify Variation in Kidney Transplant Access.Kidney international reports · 2026Article
- Scalable extraction of social determinants of health from clinical notes in a sepsis cohort using instruction-tuned language models.JAMIA open · 2026Article
- Emerging applications of traditional and generative artificial intelligence in liver transplantation.Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society · 2026Article
- Article
- Development of a natural language processing algorithm to extract social determinants of health from clinician notes.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Psychosocial risk factors and social determinants of health (SDOH) contribute to persistent disparities in liver transplantation access. We developed a large language model framework to extract and analyze how these factors influence care trajectories. Prevalence of key modifiable barriers varied by demographics: social support gaps (35.4%, disproportionately affecting females), recent substance use (14.2-22.7%), and mental health challenges (17.6%, with Hispanic/Latino treatment gaps). Each factor was associated with 5-14 percentage point reductions in listing probability, comparable to clinical metrics. Psychosocial risk and SDOH factors explained 42.6% of racial disparities in listing decisions for Asian patients, exceeding liver health metrics (36.8%) and contributing to 94.6% collective explanation of differences. Priority interventions should target caregiver support, substance use, mental health, and patient education. This framework for systematically analyzing patient circumstances could enhance understanding of care decisions and health disparities.
Identifiers
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Registered trials
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.