Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 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
6 authors.
Mengxian LyuDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Cheng PengDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Xiaohan LiDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Patrick BalianDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Jiang BianDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Yonghui WuDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Funding
ACTS (AD Clinical Trial Simulation): Developing Advanced Informatics Approaches for an Alzheimer's Disease Clinical Trial Simulation SystemR01AG084236 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Jiang Bian, Cui Tao · 2023 to 2026
$4.1M
Computational Drug Repurposing for AD/ADRD with Integrative Analysis of Real World Data and Biomedical KnowledgeR01AG076234 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Jiang Bian, Fei Wang · 2022 to 2026
$3.8M
An end-to-end informatics framework to study Multiple Chronic Conditions (MCC)'s impact on Alzheimer's disease using harmonized electronic health recordsR01AG083039 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI BIAN, JIANG, JIANG, XIAOQIAN · 2023 to 2025
$3.4M
Eligibility criteria design for Alzheimer's trials with real-world data and explainable AIR01AG080991 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Jiang Bian, Fei Wang · 2023 to 2026
$3.1M
Disparities of Alzheimer's disease progression in Sexual Minority IndividualsR01AG080624 · NIA · UNIVERSITY OF FLORIDA · PI Jiang Bian, Yi Guo · 2023 to 2026
$3.1M
Artificial Intelligence and Counterfactually Actionable Responses to End HIV (AI-CARE-HIV)R01AI172875 · NIAID · UNIVERSITY OF FLORIDA · PI Jiang Bian, Mattia Prosperi · 2023 to 2026
$2.8M
Identifying pediatric asthma subtypes using novel privacy-preserving federated machine learning methodsR01HL169277 · NHLBI · UNIVERSITY OF FLORIDA · PI Jennifer Noel Fishe, Jie Xu · 2023 to 2026
$2.7M
Post-Acute Sequelae of SARS-CoV-2 Infection and Subsequent Disease Progression in Individuals with AD/ADRD: Influence of the Social and Environmental Determinants of HealthRF1AG084178 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI BIAN, JIANG, HU, HUI · 2023 to 2023
$2.6M
The benefits and harms of lung cancer screening in FloridaR01CA246418 · NCI · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2020 to 2023
$1.7M
Advancing Drug Repositioning for Alzheimer’s Disease using Real-world DataR56AG069880 · NIA · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, CHEN, YONG · 2021 to 2022
Automatic text summarization (ATS) is an emerging technology to assist clinicians in providing continuous and coordinated care. This study presents an approach to summarize doctor-patient dialogues using generative large language models (LLMs). We developed prompt-tuning algorithms to instruct generative LLMs to summarize clinical text. We examined the prompt-tuning strategies, the size of soft prompts, and the few-short learning ability of GatorTronGPT, a generative clinical LLM developed using 277 billion clinical and general English words with up to 20 billion parameters. We compared GatorTronGPT with a previous solution based on fine-tuning of a widely used T5 model, using a clinical benchmark dataset MTS-DIALOG. The experimental results show that the GatorTronGPT-20B model achieved the best performance on all evaluation metrics. The proposed solution has a low computing cost as the LLM parameters are not updated during prompt-tuning. This study demonstrates the efficiency of generative clinical LLMs for clinical ATS through prompt tuning.
Identifiers
PMID40502276
PMCPMC12150732
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.
Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning. · full record | OpenQuestion