Evidence map›Paper›PMID 35017284›Full record

ReviewNeurosciences (Riyadh, Saudi Arabia)2022

Rapid implementation of telemedicine in Neurology during the COVID-19 pandemic: Challenges in King Abdulaziz Medical City-Jeddah.

Hussein A Algahtani, Bader H Shirah

Abstract readReview
In one paragraph

Review in Neurosciences (Riyadh, Saudi Arabia), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

2 authors.

Hussein A AlgahtaniFrom the Neurology Section (Algahtani), Department of Medicine, King Abdulaziz Medical City, from King Abdullah International Medical Research Center (Algahtani), from the College of Medicine (Algahtani), King Saud bin Abdulaziz University for Health Sciences, and from Department of Neuroscience (Shirah), King Faisal Specialist Hospital & Research Centre, Jeddah, Kingdom of Saudi Arabia.
Bader H ShirahFrom the Neurology Section (Algahtani), Department of Medicine, King Abdulaziz Medical City, from King Abdullah International Medical Research Center (Algahtani), from the College of Medicine (Algahtani), King Saud bin Abdulaziz University for Health Sciences, and from Department of Neuroscience (Shirah), King Faisal Specialist Hospital & Research Centre, Jeddah, Kingdom of Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Telemedicine is defined as the remote medical practice of delivering healthcare services to the underserved using information and communication technology. It encompasses a wide range of medical activities, including diagnosis, treatment, disease prevention, and education. The coronavirus disease of 2019 (COVID-19) pandemic has caused significant social dislocation, negative economic impact, and a major change in medical practice in Saudi Arabia. Telemedicine has rapidly moved to the frontline of healthcare practice due to the demand for prevention and mitigation strategies. It has been encouraged and facilitated with huge government support. Herein, we describe the virtual clinical practice of the neurology department at King Abdulaziz Medical City-Jeddah in response to the COVID-19 pandemic. This narrative review is an urgent call to improve the perception and knowledge of both medical personnel and patients concerning telemedicine and to support the utilization of advanced information and communication technology.

Indexed as

COVID-19NeurologyTelemedicineHumansPandemicsSARS-CoV-2

Identifiers

PMID35017284
PMCPMC9037570

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.