Evidence map›Paper›PMID 27293838›Full record

ArticleJournal of thoracic disease2016

The reliability analysis of Xpert-positive result for smear-negative and culture-negative specimen collected from bone and joint tuberculosis suspects.

Guomei Wei, Jing Mu, Guirong Wang, Fengmin Huo, Lingling Dong, Yunxu Li, Hairong Huang

Open access · hybridAbstract read
In one paragraph

Article in Journal of thoracic disease, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
0.9field-weighted citation impact, top 25% 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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. XpertThe Cochrane database of systematic reviews · 2018
    Pooled it
  3. Article
  4. Article
  5. 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

7 authors at 2 institutions in 1 country.

Guomei Wei1 National Clinical Laboratory on Tuberculosis, Beijing Key laboratory for Drug-resistant Tuberculosis Research, 2 Department of Pathology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing 101149, China.
Jing Mu1 National Clinical Laboratory on Tuberculosis, Beijing Key laboratory for Drug-resistant Tuberculosis Research, 2 Department of Pathology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing 101149, China.
Guirong Wang1 National Clinical Laboratory on Tuberculosis, Beijing Key laboratory for Drug-resistant Tuberculosis Research, 2 Department of Pathology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing 101149, China.
Fengmin Huo1 National Clinical Laboratory on Tuberculosis, Beijing Key laboratory for Drug-resistant Tuberculosis Research, 2 Department of Pathology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing 101149, China.
Lingling Dong1 National Clinical Laboratory on Tuberculosis, Beijing Key laboratory for Drug-resistant Tuberculosis Research, 2 Department of Pathology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing 101149, China.
Yunxu Li1 National Clinical Laboratory on Tuberculosis, Beijing Key laboratory for Drug-resistant Tuberculosis Research, 2 Department of Pathology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing 101149, China.
Hairong Huang1 National Clinical Laboratory on Tuberculosis, Beijing Key laboratory for Drug-resistant Tuberculosis Research, 2 Department of Pathology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing 101149, China.
Beijing Chest Hospital · CNCapital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Xpert MTB/RIF assay (Xpert; Cepheid, Sunnyvale, CA, USA) has been widely used for pulmonary and extra-pulmonary tuberculosis (TB) diagnosis. In clinical practice, specimen yielding smear-negative, culture-negative but Xpert-positive results is frequently confronted. Due to the notorious possibility of contamination that molecular tests always been thought of, Xpert-positive results without bacteriological supporting evidence arouse great confusions to clinicians.

methodsA retrospective study was performed. From April 2014 to February 2015, 852 clinical specimens were Xpert-positive. The results of Xpert assay, bacteriological and pathological examinations from either the same specimens or from the specimens collected during same clinical operations were investigated.

resultsA total of 90 specimens with Xpert-positive but smear-negative and culture-negative results were recruited, and 81 of them were pus specimens collected from Bone and Joint Tuberculosis (BJTB) patients. According to the pathological examination results, 77 of the 81 pus specimens, 8 of 9 other types of specimens were confirmed as either TB or strongly suggestive of TB; three pus specimens and one biopsy tissue were also suggested TB but with less stronger evidence; only one pus specimen was not TB suggestive.

conclusionsOur study demonstrated that Xpert could be trusted for BJTB diagnosis even when no supporting bacteriological evidence is available in high TB prevalence settings. Our results will alleviate the confusion among clinicians in such scenarios.

Indexed as

pathological examinationtuberculosis (TB)Xpert MTB/RIF assay

Identifiers

PMID27293838
PMCPMC4885960
OpenAlexW2342718896

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.