Evidence map›Paper›PMID 28674427›Full record

ArticleScientific reports2017

Comparison of laboratory testing methods for the diagnosis of tuberculous pleurisy in China.

Qing Zhang, Caicun Zhou

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.8field-weighted citation impact, top 14% of its field
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

9 citing papers in PubMed, 21 citations in OpenAlex.

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  9. Biomarkers in the diagnosis of pleural diseases: a 2018 update.Therapeutic advances in respiratory disease
    Review
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

2 authors at 2 institutions in 1 country.

Qing ZhangSoochow University, Suzhou, 215006, China.
Caicun ZhouSoochow University, Suzhou, 215006, China. Zhoucaicun121316@163.com.
Shanghai Pulmonary Hospital · CNSoochow University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To evaluate the diagnostic utilities of different methods for Mycobacterium tuberculosis (M. tuberculosis) detection in pleural fluid that represent potentially cost-effective measures for resource-limited settings in China. We compared diagnostic characteristics of the AmpSure simultaneous amplification and testing method, the BACTEC MGIT 960 system, and acid-fast bacilli staining of specimen smears for detection of M. tuberculosis in pleural fluids of 438 patients with suspected tuberculous pleurisy. Chest radiographs, computed tomography scans and the results of sputum and pleural biopsy testing were used for confirmations of tuberculosis diagnoses. The sensitivity of the AmpSure test (21.2%) was significantly higher than that of smear analysis (3.1%; p < 0.001), but was similar to that of the BACTEC culture method (17.8%; p > 0.05). The specificity of the AmpSure, BACTEC, and smear tests were 100%, 96.4%, and 100%, respectively. The positive and negative predictive values of the AmpSure, BACTEC, and smear tests were 100%/23.1%; 78.6%/19.8%; and 100%/22.4%, respectively. The sensitivity of ADA, IFNγ and histopathological analyses of pleural biopsies were all 100%. The sensitivities of all three methods were suboptimal for the detection of M. tuberculosis in pleural fluid. Future studies of a composite diagnostic index consisting of a combination of these tests are warranted.

Indexed as

Mycobacterium tuberculosisAdultAgedBacteriological TechniquesBiopsyDiagnostic Tests, RoutineFemaleHumansInterferon-gamma Release TestsMaleMiddle AgedMultimodal ImagingReproducibility of ResultsSensitivity and SpecificitySputumTuberculosis, Pleural

Identifiers

PMID28674427
PMCPMC5495749
OpenAlexW2733070743

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.