Evidence map›Paper›PMID 33308664›Full record

ReviewThe Indian journal of tuberculosis2020

Benefits and limitations of serological assays in COVID-19 infection.

Zeeshan Sidiq, M Hanif, Kaushal Kumar Dwivedi, K K Chopra

Open access · greenAbstract readReview
In one paragraph

Review in The Indian journal of tuberculosis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 2 pooled it
2.0field-weighted citation impact, top 8% 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

55 citing papers in PubMed, 2 syntheses or guidelines pooled it, 109 citations in OpenAlex.

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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 at 1 institution in 1 country.

Zeeshan SidiqNew Delhi Tuberculosis Centre, JLN Marg, New Delhi, India.
M HanifNew Delhi Tuberculosis Centre, JLN Marg, New Delhi, India. Electronic address: irldlndc@rntcp.org.
Kaushal Kumar DwivediNew Delhi Tuberculosis Centre, JLN Marg, New Delhi, India.
K K ChopraNew Delhi Tuberculosis Centre, JLN Marg, New Delhi, India.
New Delhi Tuberculosis Center · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate and rapid diagnostic tests are critical for achieving control of coronavirus disease 2019 (covid-19), a pandemic illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Diagnostic tests for covid-19 fall into two main categories: molecular tests that detect viral RNA, and serological tests that detect anti-SARS-CoV-2 immunoglobulins. Reverse transcriptase polymerase chain reaction (RT-PCR), a molecular test, has become the gold standard for diagnosis of covid-19; however, this test has many limitations that include potential false negative results, changes in diagnostic accuracy over the disease course, and precarious availability of test materials. Serological tests have generated substantial interest as an alternative or complement to RT-PCR and other Nucleic acid tests in the diagnosis of acute infection, as some might be cheaper and easier to implement at the point of care. A clear advantage of these tests over RT-PCR is that they can identify individuals previously infected by SARS-CoV-2, even if they never underwent testing while acutely ill. Many serological tests for covid-19 have become available in a short period, including some marketed for use as rapid, point-of-care tests. The pace of development has, however, exceeded that of rigorous evaluation, and important uncertainty about test accuracy remains.

Indexed as

COVID-19 Serological TestingCOVID-19HumansSARS-CoV-2Sensitivity and SpecificityCOVID-19IgGIgMSARS CoV-2Serology

Identifiers

PMID33308664
PMCPMC7409828
OpenAlexW3047566583

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.