Evidence map›Paper›PMID 35924134›Full record

ReviewThe Indian journal of radiology & imaging2022

Neuroimaging Findings in COVID-19 Associated Rhino-Orbital-Cerebral Mucormycosis: A Review.

Anjuna Reghunath, Rohini Gupta Ghasi, Anuradha Sharma, Neha Bagri, Swarna Gupta Jain

Open access · diamondAbstract readReview
In one paragraph

Review in The Indian journal of radiology & imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 57% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

5 authors at 1 institution in 1 country.

Anjuna ReghunathDepartment of Radiodiagnosis, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.ORCID 0000-0002-8178-6839
Rohini Gupta GhasiDepartment of Radiodiagnosis, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.
Anuradha SharmaDepartment of Radiodiagnosis, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.
Neha BagriDepartment of Radiodiagnosis, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.
Swarna Gupta JainDepartment of Radiodiagnosis, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.
Vardhman Mahavir Medical College & Safdarjung Hospital · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The involvement of the neurological system by coronavirus has been well established. Since its onset, the systemic manifestations of coronavirus disease 2019 (COVID-19) have been evolving rapidly and imaging plays a pivotal role in diagnosing the various primary and secondary effects of the disease. As the pandemic continues to defy human civilization, secondary impacts of the disease and the treatment given to patients afflicted with the disease have stemmed up. Rhino-orbital-cerebral mucormycosis is one such potentially dangerous infection now commonly seen in COVID-19 patients, especially the ones treated with immunosuppressants. Early diagnosis is key for COVID-19-associated mucormycosis (CAM), and radiologists should be well aware of its alarming neurological manifestations from the involvement of parenchyma, meninges, vessels, cranial nerves, and skull base. This review highlights the magnetic resonance imaging features of neuraxial involvement in CAM.

Indexed as

computed tomographyCOVID-19magnetic resonance imagingmucormycosisneuroimagingROCM

Identifiers

PMID35924134
PMCPMC9340187
OpenAlexW4285394188

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.