Article in Journal of the American Medical Informatics Association : JAMIA, 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.
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.
Yudong WangThe Center for Health AI and Synthesis of Evidence (CHASE), Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA 19104, United States.
Dazheng ZhangThe Center for Health AI and Synthesis of Evidence (CHASE), Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA 19104, United States.ORCID 0000-0002-1431-0785
Jiayi TongThe Center for Health AI and Synthesis of Evidence (CHASE), Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA 19104, United States.
Xing HeDepartment of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN 46202, United States.ORCID 0000-0003-0290-8058
Liang LiDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.ORCID 0000-0001-5453-3839
Lichao SunDepartment of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, United States.
Ashutosh M ShuklaNorth Florida South Georgia Veteran Healthcare System, Gainesville, FL 32608, United States.
Jiang BianDepartment of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN 46202, United States.ORCID 0000-0002-2238-5429
David A AschDivision of General Internal Medicine, The University of Pennsylvania, Philadelphia, PA 19104, United States.
Yong ChenThe Center for Health AI and Synthesis of Evidence (CHASE), Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA 19104, United States.
Funding
Coordinating Individually Measured Phenotypes to Advance Mental Health ResearchU24MH136069 · NIMH · YALE UNIVERSITY · PI Yong Chen, Cui Tao · 2024 to 2026
$9.8M
PANDA-MSD: Predictive Analytics via Networked Distributed Algorithms for Multi-System DiseasesU01TR003709 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI BIAN, JIANG, CHEN, YONG · 2022 to 2025
$4.7M
TRiPOD: Toward Reusable Phenotypes in Observational Data for AD/ADRD - managing definitions and correcting biasR01AG073435 · NIA · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG, XU, HUA · 2021 to 2025
$3.9M
ClinEX - Clinical Evidence Extraction, Representation, and AppraisalR01LM014344 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Yong Chen, Yifan Peng · 2023 to 2026
$2.7M
Novel evidence-accumulation-driven methods for characterizing kidney stone progressionR01DK128237 · NIDDK · UT SOUTHWESTERN MEDICAL CENTER · PI LIU, YU-LUN · 2022 to 2025
$2.7M
AI-ADRD: Accelerating interventions of AD/ADRD via Machine learning methodsRF1AG077820 · NIA · UNIVERSITY OF PENNSYLVANIA · PI BIAN, JIANG, CHEN, YONG · 2023 to 2023
$2.3M
PheBC: bias correction methods for EHR derived phenotypeR01LM013519 · NLM · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG, XU, HUA · 2021 to 2024
$1.6M
CICADA: clinical informatics and computational approaches for drug-repositioning of AD/ADRDR56AG074604 · NIA · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG, TAO, CUI · 2021 to 2022
$1.5M
Surrogate Augmented Deep Predictive Learning for Retinopathy of PrematurityR21EY034179 · NEI · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG, HE, LIFANG · 2023 to 2023
$482k
Methods for Estimating Disease Burden of Seasonal InfluenzaR21AI167418 · NIAID · EMORY UNIVERSITY · PI CHANG, HOWARD H, CHEN, YONG · 2023 to 2024
objectivePatients of different race have different outcomes following renal transplantation. Patients of different race also undergo renal transplantation at different hospitals. We used a novel decentralized multisite approach to quantitatively assess the effect of site of care on racial disparities between non-Hispanic Black (NHB) and non-Hispanic White (NHW) patients in post-transplantation survival times. MATERIALS AND
methodsIn this study, we develop a communication-efficient federated learning algorithm to assess site-of-care associated racial disparities based on decentralized time-to-event data, called Communication-Efficient Distributed Analysis for Racial Disparity in Time-to-event Data (CEDAR-t2e). The algorithm includes 2 modules. Module I is to estimate the site-specific proportional hazards model for time-to-event outcomes in a distributed manner, in which the Poissonization is used to simplify the estimation procedure. Based on the estimated results from Module I, Module II calculates how long the kidney failure time of NHB patients would be extended had they been admitted to transplant centers in the same distribution as NHW patients were admitted.
resultsWith application to United States Renal Data System data covering 39 043 patients across 73 transplant centers, we found no evidence suggesting the presence of site-of-care associated racial disparities in post-transplantation survival times. In particular, restricting to one year after transplantation, the counterfactual graft failure time would have been extended by only 0.61 days on average if NHB had the same admission distribution to transplant centers as NHW patients. DISCUSSION: The proposed approach offers a quantitative measure to evaluate site-of-care associated racial disparities.
conclusionOur approach has the potential to be extended to investigate site-of-care related disparities in other time-to-event outcomes, thus promoting health equity and improving patient health in various fields.
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.
A communication-efficient federated learning algorithm to assess racial disparities in post-transplantation survival time. · full record | OpenQuestion