Evidence map›Paper›PMID 37034419›Full record

ArticleFrontiers in research metrics and analytics2023

The research foundation for COVID-19 vaccine development.

Komi S Messan, Pawel P Sulima, Dolan Ghosh, Jonathan Nye

Open access · goldAbstract read
In one paragraph

Article in Frontiers in research metrics and analytics, 2023. 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
1.0field-weighted citation impact, top 27% 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, 5 citations in OpenAlex.

  1. 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

4 authors at 2 institutions in 1 country.

Komi S MessanData Analytics and Research Branch, National Institutes of Health, National Institute of Allergy and Infectious Diseases, Office of Strategic Planning Initiative Development and Analysis, Rockville, MD, United States.
Pawel P SulimaData Analytics and Research Branch, National Institutes of Health, National Institute of Allergy and Infectious Diseases, Office of Strategic Planning Initiative Development and Analysis, Rockville, MD, United States.
Dolan GhoshNational Institutes of Health, Office of the Director, NIH Office of Extramural Research, Bethesda, MD, United States.
Jonathan NyeData Analytics and Research Branch, National Institutes of Health, National Institute of Allergy and Infectious Diseases, Office of Strategic Planning Initiative Development and Analysis, Rockville, MD, United States.
National Institutes of Health · USOffice of Extramural Research · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of effective vaccines in <1 year to combat the spread of coronavirus disease 19 (COVID-19) is an example of particularly rapid progress in biomedicine. However, this was only made possible by decades of investment in scientific research. Many important research commentaries and reviews have been provided to describe the various contributions and scientific breakthroughs that led to the development of COVID-19 vaccines. In this work, we sought to complement those efforts by adding a systematic and quantitative study of the research foundations that led to these vaccines. Here, we analyzed citations from COVID-19 vaccine research articles to determine which scientific areas of study contributed the most to this research. Our findings revealed that coronavirus research was cited most often, and by a large margin. However, significant contributions were also seen from a diverse set of fields such as cancer, diabetes, and HIV/AIDS. In addition, we examined the publication history of the most prolific authors of COVID-19 vaccine research to determine their research expertise prior to the pandemic. Interestingly, although COVID-19 vaccine research relied most heavily on previous coronavirus work, we find that the most prolific authors on these publications most often had expertise in other areas including influenza, cancer, and HIV/AIDS. Finally, we used machine learning to identify and group together publications based on their major topic areas. This allowed us to elucidate the differences in citations between research areas. These findings highlight and quantify the relevance of prior research from a variety of scientific fields to the rapid development of a COVID-19 vaccine. This study also illustrates the importance of funding and sustaining a diverse research enterprise to facilitate a rapid response to future pandemics.

Indexed as

citation analysisCOVID-19 vaccinemachine learningnatural language processingtext mining

Identifiers

PMID37034419
PMCPMC10080055
OpenAlexW4360866276

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.