ArticleKidney international reports2026
Leveraging Natural Language Processing to Identify Variation in Kidney Transplant Access.
Article in Kidney international reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Introduction: There are significant sex, racial, ethnic, and socioeconomic disparities in kidney transplantation in the United States, but upstream factors driving these disparities are understudied because prewaitlisting data are not yet routinely collected by national data surveillance systems. Methods: To address this gap, we developed a rule-based semiautomated natural language processing (NLP) pipeline to extract prewaitlist milestones from nephrologist and social worker clinical notes at a nonprofit dialysis organization in New York City. Our outcomes were (i) transplant discussed with the patient; (ii) patient interest in transplant; (iii) transplant referral; and (iv) receipt of a kidney transplant. In a retrospective observational cohort study of patients with incident end-stage kidney disease (ESKD) receiving dialysis, we evaluated sex-based, racial and ethnic, and socioeconomic variation (on the basis of primary insurance payer and census block group-level Area Deprivation Index [ADI]) in the outcomes of interest using multivariable logistic regression and Cox models. Results: Our NLP pipeline showed excellent precision, recall, and F1 scores > 0.8. Among 2624 patients, documentation of transplant discussion (adjusted subhazard ratio [sHR]: 1.30; 95% confidence interval [CI]: 1.01-1.67) and patient interest (sHR: 2.11; 95% CI: 1.65-2.69) were associated with a greater likelihood of receiving a kidney transplant. Patients with Medicaid (vs. non-Medicaid, adjusted odds ratio [OR]: 0.73; 95% CI:, 0.59-0.90) were less likely to have interest in transplant documented. Sex, race and ethnicity, and ADI were not associated with time to transplant referral in adjusted analyses. Non-Hispanic Black race (adjusted sHR: 0.40; 95% CI: 0.23-0.69), Medicaid insurance (adjusted sHR: 0.73; 95% CI: 0.56-0.94), and below-median ADI (adjusted sHR: 0.77; 95% CI: 0.60-0.98) were associated with lower receipt of a kidney transplant. Conclusions: Using a novel NLP pipeline, we identified socioeconomic disparities in the documentation of patient interest in transplant, but not referrals. Patient-level interventions to increase interest in transplantation may be needed to address disparities in kidney transplantation.
Indexed as
Identifiers
What OpenQuestion holds
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.