Evidence map›Paper›PMID 41858625›Full record

ArticleiScience2026

A machine learning model for predicting adverse prognostic events in patients with neurosyphilis: Results from the DEFEAT-NS study.

Zhen Lu, Jun Zou, Hanlin Zhang, Meiyin Zou, Yanhua Fu, Renfang Zhang, Haoran Shi, Weibo Wu, Bichen Xue, Ruonan Wang and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the 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

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

20 authors.

Zhen LuSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China.
Jun ZouThe Fourth People's Hospital of Nanning, Nanning 530021, Guangxi, China.
Hanlin ZhangDepartment of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
Meiyin ZouDepartment of Infectious Diseases, Nantong Third People's Hospital, Affiliated Nantong Third Hospital of Nantong University, Nantong, Jiangsu 226006, China.
Yanhua FuGuiyang Public Health Clinical Center, Guiyang 550004, China.
Renfang ZhangShanghai Public Health Clinical Center, Fudan University, Shanghai, China.
Haoran ShiDepartment of Infectious Diseases, Nantong Third People's Hospital, Affiliated Nantong Third Hospital of Nantong University, Nantong, Jiangsu 226006, China.
Weibo WuThe Third People's Hospital of Shenzhen, Shenzhen, Guangdong 518112, China.
Bichen XueShanghai Public Health Clinical Center, Fudan University, Shanghai, China.
Ruonan WangDepartment of Infectious Diseases, Nantong Third People's Hospital, Affiliated Nantong Third Hospital of Nantong University, Nantong, Jiangsu 226006, China.
Xiaoyan YangGuiyang Public Health Clinical Center, Guiyang 550004, China.
Jing CaiSchool of Public Health, Shandong University, Jinan 250100, China.
Lin GanGuiyang Public Health Clinical Center, Guiyang 550004, China.
Shangbin LiuShanghai Jiao Tong University School of Medicine, Tongren Hospital, Center for Public Health Research, Shanghai, China.
Yong CaiShanghai Jiao Tong University School of Medicine, Tongren Hospital, Center for Public Health Research, Shanghai, China.
Zhihang PengNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Jun LiDepartment of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
Liuqing YangThe Third People's Hospital of Shenzhen, Shenzhen, Guangdong 518112, China.
Jun ChenShanghai Public Health Clinical Center, Fudan University, Shanghai, China.
Huachun ZouShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, 131 Dong'an Road, Shanghai 200032, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying patients at highest risk of serious adverse prognostic events (AE) in neurosyphilis could enable risk-stratified treatment beyond clinical judgment. We developed machine-learning models using electronic health records from six Chinese infectious-diseases hospitals, with two centers for external validation and four for discovery. Five models incorporated demographic, clinical, laboratory, and treatment variables from 602 observations (402 discovery, 200 validation). AE occurred in 20.90% and 20.50%, respectively. DEFEAT-NS-M1 achieved AUROC 0.975 (95% CI 0.949-0.995) internally and 0.863 (0.801-0.920) externally, with Brier scores 0.027 and 0.128. Decision curve analysis demonstrated favorable clinical utility; treating 1-2 high-risk patients prevents one AE. DEFEAT-NS-M1 supports population-level risk estimation and stratified care, potentially guiding targeted monitoring and therapy. Further external validation and health-economic assessment are warranted.

Indexed as

Artificial intelligenceNeurologyPreventive medicine

Identifiers

PMID41858625
PMCPMC12995694

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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.