Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing 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
4 authors.
Can LiDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Dejian LaiDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Xiaoqian JiangDepartment of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Kai ZhangDepartment of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Funding
RADx-Rad Discoveries & Data: Consortium Coordination Center Program OrganizationU24LM013755 · NLM · YALE UNIVERSITY · PI ARONOFF-SPENCER, ELIAH S, OHNO-MACHADO, LUCILA · 2021 to 2023
$23.3M
iDASH: Integrating Data for Analysis, Anonymization, and SharingU54HL108460 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI OHNO-MACHADO, LUCILA · 2010 to 2015
$19.1M
Ethics Core (FABRIC)U54HG012510 · NHGRI · YALE UNIVERSITY · PI OHNO-MACHADO, LUCILA · 2022 to 2025
$11.2M
Open Health Natural Language Processing CollaboratoryU01TR002062 · NCATS · MAYO CLINIC ROCHESTER · PI JIANG, XIAOQIAN, LIU, HONGFANG · 2017 to 2021
$7.6M
AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer's disease via deep learningU01AG079847 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Christopher A. Gaiteri, Xiaoqian Jiang · 2023 to 2026
$5.1M
Finding Combinatorial Drug Repositioning Therapy For Alzheimer'S Disease And Related DementiasR01AG066749 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI JIANG, XIAOQIAN, ZHENG, WENJIN JIM · 2020 to 2024
$4.4M
Accelerating Genomic Data Sharing and Collaborative Research with Privacy ProtectionR01LM014520 · NLM · CASE WESTERN RESERVE UNIVERSITY · PI Erman Ayday, Xiaoqian Jiang · 2023 to 2026
$2.6M
Decentralized differentially-private methods for dynamic data release and analysisR01LM013712 · NLM · YALE UNIVERSITY · PI JIANG, XIAOQIAN, OHNO-MACHADO, LUCILA · 2022 to 2025
$2.5M
Harmonizing multiple clinical trials for Alzheimer's disease to investigate differential responses to treatment via federated counterfactual learningR01AG082721 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Xiaoqian Jiang, Yejin Kim · 2023 to 2026
$2.0M
Robust privacy preserving distributed analysis platform for cancer research: addressing data bias and disparitiesU01CA274576 · NCI · UNIVERSITY OF PENNSYLVANIA · PI JIANG, XIAOQIAN, LONG, QI · 2023 to 2025
Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes such as age group, gender, and race/ethnicity. Machine learning models for outcome prediction can introduce additional biases. Therefore, we introduce
Identifiers
PMID38827050
PMCPMC11141863
What OpenQuestion holds
Textmetadata
Read underepoch 390
Registered trials
None linked
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.
FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation. · full record | OpenQuestion