Evidence map›Paper›PMID 33374612›Full record

ReviewDiagnostics (Basel, Switzerland)2020

Point of Care Diagnostics in the Age of COVID-19.

Meysam Rezaei, Sajad Razavi Bazaz, Sareh Zhand, Nima Sayyadi, Dayong Jin, Martin P Stewart, Majid Ebrahimi Warkiani

Open access · goldAbstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
1.4field-weighted citation impact, top 10% 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

31 citing papers in PubMed, 66 citations in OpenAlex.

  1. Article
  2. UltraNet: Unleashing the Power of Simplicity for Accurate Medical Image Segmentation.Interdisciplinary sciences, computational life sciences · 2025
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Recent advancements in critical care: Part I.Indian journal of anaesthesia · 2023
    Article
  10. Article
  11. Article
  12. Miniaturized Real-Time PCR systems for SARS-CoV-2 detection at the Point-of-Care.Clinica chimica acta; international journal of clinical chemistry · 2022
    Review
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Prognosis patients with COVID-19 using deep learning.BMC medical informatics and decision making · 2022
    Article
  20. OraSure InteliSwabViruses · 2022
    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 1 institution in 3 countries.

Meysam RezaeiSchool of Biomedical Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia.ORCID 0000-0002-3111-7231
Sajad Razavi BazazSchool of Biomedical Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia.ORCID 0000-0002-6419-3361
Sareh ZhandSchool of Biomedical Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia.
Nima SayyadiSchool of Biomedical Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia.
Dayong JinInstitute for Biomedical Materials & Devices (IBMD), Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia.
Martin P StewartInstitute for Biomedical Materials & Devices (IBMD), Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia.
Majid Ebrahimi WarkianiSchool of Biomedical Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia.
University of Technology Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recent outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated serious respiratory disease, coronavirus disease 2019 (COVID-19), poses a major threat to global public health. Owing to the lack of vaccine and effective treatments, many countries have been overwhelmed with an exponential spread of the virus and surge in the number of confirmed COVID-19 cases. Current standard diagnostic methods are inadequate for widespread testing as they suffer from prolonged turn-around times (>12 h) and mostly rely on high-biosafety-level laboratories and well-trained technicians. Point-of-care (POC) tests have the potential to vastly improve healthcare in several ways, ranging from enabling earlier detection and easier monitoring of disease to reaching remote populations. In recent years, the field of POC diagnostics has improved markedly with the advent of micro- and nanotechnologies. Due to the COVID-19 pandemic, POC technologies have been rapidly innovated to address key limitations faced in existing standard diagnostic methods. This review summarizes and compares the latest available POC immunoassay, nucleic acid-based and clustered regularly interspaced short palindromic repeats- (CRISPR)-mediated tests for SARS-CoV-2 detection that we anticipate aiding healthcare facilities to control virus infection and prevent subsequent spread.

Indexed as

COVID-19point of care testingrespiratory diseasesSARS-CoV-2

Identifiers

PMID33374612
PMCPMC7822494
OpenAlexW3118077959

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.