Evidence map›Paper›PMID 40995125›Full record

ArticleiScience2025

International multicenter development of ensemble machine learning driven host response based diagnosis for tuberculosis.

Shufa Zheng, Wenxin Qu, Dan Zhang, Jieting Zhou, Yifan Xu, Wei Wu, Chang Liu, Mingzhu Huang, Enhui Shen, Xiao Chen and 5 more

Abstract read
In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
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

15 authors.

Shufa ZhengDepartment of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Wenxin QuDepartment of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Dan ZhangDepartment of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Jieting ZhouDepartment of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Yifan XuDepartment of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Wei WuState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Chang LiuDepartment of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Mingzhu HuangDepartment of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Enhui ShenInstitute of Bioinformatics, Zhejiang University, Hangzhou, China.
Xiao ChenDepartment of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Michael P TimkoDepartments of Biology and Public Health Sciences, University of Virginia, Charlottesville, VA 22904, USA.
Longjiang FanInstitute of Bioinformatics, Zhejiang University, Hangzhou, China.
Fei YuDepartment of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Dongsheng HanDepartment of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Yifei ShenDepartment of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Active pulmonary tuberculosis (TB) is challenging to diagnose, and monitoring treatment response effectively remains difficult. To address these challenges, we developed TB-Scope, a host-gene-expression-based ensemble machine learning classification model. Using large-scale microarray datasets (

Indexed as

Machine learningRespiratory medicine

Identifiers

PMID40995125
PMCPMC12455115

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.