Evidence map›Paper›PMID 41415255›Full record

ArticleFrontiers in public health2025

Analysis of influencing factors and construction of prediction model for multidrug-resistant tuberculosis in Nanning area.

Jie Huang, Qing-Dong Zhu, Kan Xie, Ting-Ting Lu, Xing-Fa Lu, Jie-Ling Chen, Hai-Ling Yu, Yan-Ling Hu

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Jie HuangFaculty of Data Science, City University of Macau, Macau, China.
Qing-Dong ZhuHIV/AIDS Clinical Treatment Center of Guangxi (Nanning) and The Fourth People's Hospital of Nanning, Guangxi, China.
Kan XieHIV/AIDS Clinical Treatment Center of Guangxi (Nanning) and The Fourth People's Hospital of Nanning, Guangxi, China.
Ting-Ting LuHIV/AIDS Clinical Treatment Center of Guangxi (Nanning) and The Fourth People's Hospital of Nanning, Guangxi, China.
Xing-Fa LuHIV/AIDS Clinical Treatment Center of Guangxi (Nanning) and The Fourth People's Hospital of Nanning, Guangxi, China.
Jie-Ling ChenHIV/AIDS Clinical Treatment Center of Guangxi (Nanning) and The Fourth People's Hospital of Nanning, Guangxi, China.
Hai-Ling YuHIV/AIDS Clinical Treatment Center of Guangxi (Nanning) and The Fourth People's Hospital of Nanning, Guangxi, China.
Yan-Ling HuFaculty of Data Science, City University of Macau, Macau, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to analyze the characteristics of multidrug-resistant Methods: This study retrospectively analyzed all sputum specimens from pulmonary tuberculosis patients collected at the Fourth People's Hospital of Nanning from January 2021 to June 2022, including a total of 337 strains of Results: The results of binary logistics regression analysis indicated that treatment status and high-risk population were independent factors influencing multidrug resistance ( Conclusion: This study results provide a basis for precise prevention and control of multidrug-resistant tuberculosis bacteria in Nanning, help reduce the risk of transmission, and ensure public health safety of local and surrounding populations.

Indexed as

Tuberculosis, Multidrug-ResistantAdultAntitubercular AgentsChinaFemaleHumansLogistic ModelsMaleMiddle AgedMycobacterium tuberculosisRetrospective StudiesRisk FactorsROC CurveSputumAntitubercular Agentsdrug resistanceMycobacteriumpredictive modelreceiver operating characteristictuberculosis

Identifiers

PMID41415255
PMCPMC12708887

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.