Evidence map›Paper›PMID 41767560›Full record

ArticleFrontiers in microbiology2026

Real-world performance of iFIND-TBR for rapid detection of

Xichao Ou, Huiwen Zheng, Yan Li, Jiaojian Zeng, Lin Huang, Bing Zhao, Feina Li, Hui Xia, Yajie Guo, Ruida Xing and 5 more

Abstract read
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In one paragraph

Article in Frontiers in microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

15 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.
Huiwen Zheng *Laboratory of Respiratory Diseases, Beijing Key Laboratory of Core Technologies for the Prevention and Treatment of Emerging Infectious Diseases in Children, Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children's Hospital, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, Capital Medical University, Beijing, China.
Yan Li *Institute of Tuberculosis Prevention and Control, Chengde Municipal Center for Disease Control and Prevention (Chengde Municipal Health Supervision Institute), Chengde, China.
Jiaojian ZengInstitute of Tuberculosis Prevention and Control, Chengde Municipal Center for Disease Control and Prevention (Chengde Municipal Health Supervision Institute), Chengde, China.
Lin HuangInstitute of Tuberculosis Prevention and Control, Chengde Municipal Center for Disease Control and Prevention (Chengde Municipal Health Supervision Institute), Chengde, 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.
Feina LiLaboratory of Respiratory Diseases, Beijing Key Laboratory of Core Technologies for the Prevention and Treatment of Emerging Infectious Diseases in Children, Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children's Hospital, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, Capital Medical University, 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.
Yajie GuoLaboratory of Respiratory Diseases, Beijing Key Laboratory of Core Technologies for the Prevention and Treatment of Emerging Infectious Diseases in Children, Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children's Hospital, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, Capital Medical University, 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.
Yuying ChenLaboratory of Respiratory Diseases, Beijing Key Laboratory of Core Technologies for the Prevention and Treatment of Emerging Infectious Diseases in Children, Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children's Hospital, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, Capital Medical University, Beijing, China.
Zhonghua QinCenter for Accurate Detection of Tuberculosis, Tianjin Haihe Hospital, Tianjin, China.
Lixia ZhangCenter for Accurate Detection of Tuberculosis, Tianjin Haihe Hospital, Tianjin, 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.
Yingzi MaInstitute of Tuberculosis Prevention and Control, Chengde Municipal Center for Disease Control and Prevention (Chengde Municipal Health Supervision Institute), Chengde, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To evaluate the clinical diagnostic ability of iFIND TBR (iFIND) for Methods: Sputum samples, prospectively collected from patients with suspected pulmonary tuberculosis between November 2023 and December 2024, were used for comprehensive laboratory testing, including smear microscopy, solid culture, Xpert MTB/RIF, iFIND assays, and proportion method drug susceptibility testing (DST). Results: Among the 452 patients, the positive rates of iFIND (80.31%) and Xpert (76.99%) were significantly higher than those of solid culture (65.49%). Based on solid culture as the reference standard, the sensitivity of iFIND for detection of MTB was slightly higher than that of Xpert, but no statistically significant difference was observed ( Conclusion: The iFIND assay is a rapid and automated assay with sensitivity and specificity comparable to Xpert for early TB diagnosis and drug resistance screening, making it particularly suitable for implementation in primary healthcare settings and general hospitals.

Indexed as

molecular diagnosisMycobacterium tuberculosisrapidresistancerifampin

Identifiers

PMID41767560
PMCPMC12937132

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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.