Evidence map›Paper›PMID 32986020›Full record

ArticleThe Journal of clinical investigation2020

Getting to the point in point-of-care diagnostics for tuberculosis.

Elisa H Ignatius, Keira A Cohen, William R Bishai

Open access · bronzeAbstract readComment
In one paragraph

Article in The Journal of clinical investigation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.1field-weighted citation impact, top 41% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Elisa H IgnatiusDivision of Infectious Diseases.
Keira A CohenDivision of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
William R BishaiDivision of Infectious Diseases.
Office of Infectious Diseases · USJohns Hopkins University · US

Funding

Mycobacterium tuberculosis Survival Regulatory GenesR01AI037856 · NIAID · JOHNS HOPKINS UNIVERSITY · PI BISHAI, WILLIAM RAMSES · 1997 to 2020
$7.2M
Clinical Pharmacology Training ProgramT32GM066691 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI Matthew Michael Ippolito · 2003 to 2026
$6.4M
Improved Tools for Nontuberculous Mycobacteria Lung DiseaseK08HL139994 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI COHEN, KEIRA ALEXIS · 2018 to 2022
$794k
NHLBI NIH HHS K08 HL139994NIAID NIH HHS R01 AI037856NIGMS NIH HHS T32 GM066691
6 · The paper itself

Abstract

Tuberculosis (TB) continues to affect over 10 million people per year worldwide. Despite advances in diagnosis, smear microscopy insufficiently detects pulmonary disease, with test result reporting taking longer than a day. While urine assays to detect the lipopolysaccharide lipoarabinomannan (LAM), present in mycobacterial cell walls, can provide results within minutes, the currently available assay has low sensitivity and its application is limited to patients with HIV suspected of having TB. In this issue of the JCI, Broger and Nicol et al. investigated 3 rapid urine tests in 372 ambulatory HIV-negative individuals suspected of having TB in South Africa and Peru. FujiLAM emerged as a rapid test to confirm TB diagnosis in the HIV-seronegative population. This study shows that FujiLAM has considerable potential to reshape the TB diagnostics landscape, making diagnosis and treatment in one office visit a reality for TB.

Indexed as

HIV InfectionsTuberculosisHumansLipopolysaccharidesOutpatientsPoint-of-Care SystemsPoint-of-Care TestingSensitivity and SpecificitySouth AfricalipoarabinomannanLipopolysaccharides

Identifiers

PMID32986020
PMCPMC7598045
OpenAlexW3089204130

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.