Evidence map›Paper›PMID 33072058›Full record

ArticleFrontiers in microbiology2020

Usefulness of Xpert MTB/RIF Ultra to Rapidly Diagnose Sputum Smear-Negative Pulmonary Tuberculosis Using Bronchial Washing Fluid.

Jung-Yien Chien, Ching-Kai Lin, Chong-Jen Yu, Po-Ren Hsueh

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 3 pooled it
–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

11 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Diagnostic accuracy of Xpert MTB/RIF Ultra for detecting pulmonary tuberculosis and rifampicin resistance: a systematic review and meta-analysis.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025
    Pooled it
  3. Evaluating the efficacy of stool sample on Xpert MTB/RIF Ultra and its comparison with other sample types by meta-analysis for TB diagnostics.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2022
    Pooled it
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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

4 authors.

Jung-Yien ChienDepartment of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.
Ching-Kai LinDepartment of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.
Chong-Jen YuDepartment of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.
Po-Ren HsuehDepartment of Laboratory Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluated the performance of the Xpert MTB/RIF Ultra assay (Xpert Ultra) to detect smear-negative pulmonary tuberculosis (PTB). Xpert Ultra assay was prospectively performed using bronchial washing fluid (BWF) in comparison to COBAS TaqMan MTB (COBAS) assay and mycobacterial culture. Of the 165 enrolled participants, 27 (16.4%) had PTB based on composite reference standard and 16 (9.7%) had culture-confirmed PTB. By the composite reference standard of PTB, the sensitivity of Xpert Ultra (63.0, 95% confidence interval, CI, 42.4-80.6%) was higher than the COBAS assay (25.9%,

Indexed as

bronchoscopyCOBAS TaqManMTBpulmonary tuberculosisXpert MTB/RIF Ultra

Identifiers

PMID33072058
PMCPMC7536285

What OpenQuestion holds

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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.