Evidence map›Paper›PMID 40463437›Full record

ArticleFrontiers in microbiology2025

Diagnostic value of a novel automated simultaneous amplification and testing method in pulmonary tuberculosis and extrapulmonary tuberculosis.

Xichao Ou, Peilei Hu, Xundi Bao, Zhou Liu, Chong Teng, Jingwei Guo, Dongfang Xu, Yue Li, Bing Zhao, Ruida Xing and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in microbiology, 2025. 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

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

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

19 authors.

Xichao Ou *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, China.
Peilei Hu *Department of Clinical Laboratory, Hunan Chest Hospital, Changsha, China.
Xundi Bao *Department of Clinical Laboratory, Anhui Chest Hospital, Hefei, China.
Zhou LiuDepartment of Clinical Laboratory, Anhui Chest Hospital, Hefei, China.
Chong TengDepartment of Tuberculosis, Beijing Dongcheng District Center for Disease Control and Prevention, Beijing, China.
Jingwei GuoDepartment of Clinical Laboratory, Hunan Chest Hospital, Changsha, China.
Dongfang XuDepartment of Clinical Laboratory, Anhui Chest Hospital, Hefei, China.
Yue LiDepartment of Clinical Laboratory, Anhui Chest Hospital, Hefei, China.
Bing ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, China.
Ruida XingNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, China.
Hui XiaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, China.
Ling MaInstitute of Tuberculosis Prevention and Control, Gansu Provincial Center for Disease Control and Prevention, Lanzhou, China.
Yang ZhouNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, China.
Yang ZhengNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, China.
Yuanyuan SongNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, China.
Shengfen WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, China.
Yanlin ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, China.
Yunhong TanDepartment of Tuberculosis, Beijing Dongcheng District Center for Disease Control and Prevention, Beijing, China.
Huiwen ZhengBeijing Key Laboratory of Pediatric Respiratory Infection Diseases, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, Laboratory of Respiratory Diseases, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To evaluate the clinical diagnostic ability of automated simultaneous amplification and testing (AutoSAT) method in suspected pulmonary tuberculosis (PTB) and extrapulmonary tuberculosis (EPTB) by comparing with Xpert MTB/RIF (Xpert) method against the composite reference standard. Method: Patients with suspected PTB or EPTB were enrolled consecutively from two provincial tuberculosis designated hospitals between August 2022 and December 2023. Clinical specimens were collected for routine clinical tests and AutoSAT. Results: The 297 patients including 90 PTB suspects and 207 EPTB suspects were eligible for final analysis. Among the 63 confirmed PTB, the sensitivity of Xpert was significantly higher than that of AutoSAT (30.16% vs. 28.57%, Conclusion: AutoSAT is an accurate, sensitive and rapid method for the detection of Mycobacterium tuberculosis in both PTB and EPTB patients. AutoSAT is highly effective in bacteriologically confirmed PTB, and outperforms Xpert in paucibacillary EPTB patients, especially for pleural fluid specimen detection.

Indexed as

AutoSATdiagnosisextrapulmonary tuberculosispulmonary tuberculosisXpert MTB/RIF

Identifiers

PMID40463437
PMCPMC12131008

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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.