Evidence map›Paper›PMID 42310091›Full record

ArticleNPJ digital medicine2026

Large language models for acute coronary syndrome triage at first medical contact in emergency departments.

Xuliang Wang, Wenyao Wang, Ling Li, Jingjia Wang, Xiangyu Yan, Paula Pei Li, Jun Lv, Canqing Yu, Da Liu, Chunli Shao and 11 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06493175 (The Application of Large Language Model in Emergency Chest Pain Triage), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

NCT06493175 nacompletednot on this map

The Application of Large Language Model in Emergency Chest Pain Triage

TypeinterventionalSponsorPeking University Third HospitalRan2023 to 2026Enrolled1,189ConditionsChest PainArmsApplication of large language model in emergency chest pain triage., According to the normal procedures to receive medical treatment
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

21 authors.

Xuliang Wang *Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
Wenyao Wang *Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
Ling Li *Institute of Medical Information & Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jingjia WangDepartment of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
Xiangyu YanSchool of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
Paula Pei LiInstitute for Hospital Management, Tsinghua University, Beijing, China.
Jun LvDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
Canqing YuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
Da LiuDepartment of Cardiology and Institute of Vascular Medicine, Shenzhen Third People's Hospital, Shenzhen, China.
Chunli ShaoDepartment of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
Jing ChenState Key Laboratory of Cardiovascular Disease, Department of Cardiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Kuo ZhangState Key Laboratory of Cardiovascular Disease, Department of Cardiology, Fuwai Hospital, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Hua XuDivision of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology, Hong Kong, China.
Guan WangTsinghua-Berkeley Shenzhen Institute, Shenzhen, China.
Mingqi ZhengDepartment of Cardiology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Mengdan MiaoDepartment of Cardiology, Handan First Hospital, Handan, China.
Shuanli XinDepartment of Cardiology, Handan First Hospital, Handan, China.
Chuanjie WuDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Yuwei WuThe Second Dental Center, Peking University School and Hospital of Stomatology, Beijing, China. yuweiwu@bjmu.edu.cn.
Hongxing LuoInstitute for Surgical Research, Oslo University Hospital, Rikshospitalet, Oslo, Norway. hi.hongxing@gmail.com.
Xiangbin MengPengcheng Laboratory, Shenzhen, China. chinamengxiang@gmail.com.

Funding

Beijing Natural Science Foundation Grant No. L242135Clinical Application of Medical Artificial Intelligence Project of the Hospital Management Institute of the National Health Commission Grant Nos. YLXX24AIA008, YLXX24AIA026Hebei Provincial Government Funded Clinical Talent Project Grant No. ZF2025062Hebei Provincial Higher Education Science and Technology Research Project Grant No. CXZX2025030National Key Research and Development Program for Government-to-Government International Scientific and Technological Cooperation Grant No. 2024YFE0107100
6 · The paper itself

Abstract

Acute coronary syndrome triage at first medical contact begins with patient narratives, while definitive diagnosis often requires waiting for cardiac troponin or electrocardiogram results. We developed TriageMaster-70B, a large language model using only patient narratives and vital signs for the triage. In 16,428 retrospective and 512 prospective cases, it showed high sensitivity, with per-case processing 39% faster than independent retrospective cardiologist review. These findings suggest TriageMaster-70B may support rapid, accurate, and interpretable triage in emergency departments. Clinical trial registration: ClinicalTrials.gov NCT06493175; first posted September 20, 2024.

Identifiers

PMID42310091
PMCPMC13635130

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.