Evidence map›Paper›PMID 33469323›Full record

ArticleInfection and drug resistance2021

Diagnostic Yield of Oral Swab Testing by TB-LAMP for Diagnosis of Pulmonary Tuberculosis.

Yanhua Song, Yifeng Ma, Rongmei Liu, Yuanyuan Shang, Liping Ma, Fengmin Huo, Yunxu Li, Wei Shu, Yufeng Wang, Mengqiu Gao and 1 more

Open access · goldAbstract read
In one paragraph

Article in Infection and drug resistance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 4 pooled it
2.0field-weighted citation impact, top 12% 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

34 citing papers in PubMed, 4 syntheses or guidelines pooled it, 41 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. Article
  12. High-sensitivity detection ofJournal of clinical microbiology · 2025
    Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. High-sensitivity detection ofmedRxiv : the preprint server for health sciences · 2024
    Article
  18. Article
  19. Article
  20. 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

11 authors at 2 institutions in 1 country.

Yanhua Song *Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China.
Yifeng Ma *Clinical Laboratory, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Institute, Beijing, People's Republic of China.
Rongmei Liu *Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China.
Yuanyuan Shang *Department of Bacteriology and Immunology, Beijing Key Laboratory on Drug-Resistant Tuberculosis Research, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China.
Liping MaDepartment of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China.
Fengmin HuoDepartment of Bacteriology and Immunology, Beijing Key Laboratory on Drug-Resistant Tuberculosis Research, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China.
Yunxu LiDepartment of Bacteriology and Immunology, Beijing Key Laboratory on Drug-Resistant Tuberculosis Research, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China.
Wei ShuClinical Center on TB, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China.
Yufeng WangDepartment of Laboratory Quality Control, Innovation Alliance on Tuberculosis Diagnosis and Treatment (Beijing), Beijing, People's Republic of China.
Mengqiu GaoDepartment of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China.
Yu PangDepartment of Bacteriology and Immunology, Beijing Key Laboratory on Drug-Resistant Tuberculosis Research, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China.ORCID 0000-0001-6803-9807
Capital Medical University · CNCenter for Tuberculosis Control of Guangdong Province · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveA prospective study was conducted to ascertain the accuracy of oral swab specimens collected in the early morning, spot and at night for detecting pulmonary tuberculosis (TB).

methodsWe prospectively enrolled patients with symptoms suggestive of pulmonary TB in Beijing Chest Hospital. An early morning sputum specimen was collected from each patient for GeneXpert MTB/RIF (Xpert) and mycobacterial culture. In addition, three oral swabs were collected for TB-LAMP testing.

resultsWith the combined results of three oral swab specimens, the proportion of

conclusionOral swab can be used as an alternative specimen for diagnosis of pulmonary TB using TB-LAMP. Morning oral swab exhibits the highest sensitivity, and the inclusion of more specimens at different time points provides compensation in diagnostic sensitivity with single oral swab.

Indexed as

Chinadiagnosisoral swabpulmonary tuberculosisTB-LAMP

Identifiers

PMID33469323
PMCPMC7811440
OpenAlexW3118286486

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.