Evidence map›Paper›PMID 36560496›Full record

ReviewVaccines2022

SARS-CoV-2: An Analysis of the Vaccine Candidates Tested in Combatting and Eliminating the COVID-19 Virus.

Laila Elmancy, Hala Alkhatib, Anis Daou

Abstract readReview
In one paragraph

Review in Vaccines, 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. Review
  2. Article
  3. Article
  4. 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

3 authors.

Laila ElmancyPharmaceutical Sciences Department, College of Pharmacy, QU Health, Qatar University, Doha P.O. Box 2713, Qatar.
Hala AlkhatibPharmaceutical Sciences Department, College of Pharmacy, QU Health, Qatar University, Doha P.O. Box 2713, Qatar.
Anis DaouPharmaceutical Sciences Department, College of Pharmacy, QU Health, Qatar University, Doha P.O. Box 2713, Qatar.ORCID 0000-0001-8495-3076

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, is a highly contagious virus, transferable via air droplets from close human-human contact. The pandemic has led to over 6.5 million deaths worldwide, making it the largest global health crisis since the influenza pandemic in 1918. SARS-CoV-2 rapidly spread around the world, forcing the World Health Organization (WHO) to deem it a global health pandemic after three months of its initiation. The virus has wreaked havoc on many countries worldwide, overwhelming healthcare systems, hence damaging many economies. Even though research has progressed the understanding of the SARS-CoV-2 virus, the information gathered about the vaccine trials and their findings have been scarcely distributed to the public in a single study. The information available to scientists has therefore given researchers a pathway to building an efficacious vehicle to substantially decrease the spread of the virus. The vaccines formulated had many challenges due to multiple factors such as viral mutations and clinical trial delays. This paper will aim to educate readers on the processes that the vaccine candidates took, and better understand the procedures; additionally, we'll look at all candidates' findings that went into clinical trials, assessing, analyzing, and evaluating the 27 vaccine candidates that went into phase III trials and the 13 candidates that went into either phase I/II trials.

Indexed as

clinical trialspandemicSAR-CoV-2severe acute respiratory syndrome coronavirus 2vaccine candidatesvaccinesWHO

Identifiers

PMID36560496
PMCPMC9785262

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.