Evidence map›Paper›PMID 36325437›Full record

ArticleDigital health

A topic trend analysis on COVID-19 literature.

Sara Urru, Veronica Sciannameo, Corrado Lanera, Silvano Salaris, Dario Gregori, Paola Berchialla

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
–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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. The long COVID research literature.Frontiers in research metrics and analytics · 2023
    Pooled it
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. COVID-19, Cities and Inequality.Applied geography (Sevenoaks, England) · 2023
    Article
  9. 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

6 authors.

Sara UrruUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy.
Veronica SciannameoCenter of Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torino, Turin, Italy.
Corrado LaneraUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy.
Silvano SalarisUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy.
Dario GregoriUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padua, Italy.ORCID https://orcid.org/0000-0001-7906-0580
Paola BerchiallaCenter of Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torino, Turin, Italy.ORCID https://orcid.org/0000-0001-5835-5638

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: In the past 2 years, the number of scientific publications has grown exponentially. The COVID-19 outbreak hugely contributed to this dramatic increase in the volume of published research. Currently, text mining of the volume of SARS-CoV-2 and COVID-19 publications is limited to the first months of the outbreak. We aim to identify the major topics in COVID-19 literature collected from several citational sources and analyze the temporal trend from November 2019 to December 2021. Methods: We performed an extensive literature search on SARS-Cov-2 and COVID-19 publications on PubMed, Scopus, and Web of Science (WoS) and a structural topic modelling on the retrieved abstracts. The temporal trend of the recognized topics was analyzed. Furthermore, a comparison between our corpus and the COVID-19 Open Research Dataset (CORD-19) repository was performed. Results: We collected 269,186 publications and identified 10 topics. The most popular topic was related to the clinical pictures of the COVID-19 outbreak, which has a constant trend, and the least popular includes studies on COVID-19 literature and databases. "Telemedicine", "Vaccine development", and "Epidemiology" were popular topics in the early phase of the pandemic; increasing topics in the last period are "COVID-19 impact on mental health", "Forecasting", and "Molecular Biology". "Education" was the second most popular topic, which emerged in September 2020. Conclusions: We identified 10 topics for classifying COVID-19 research publications and estimated a nonlinear temporal trend that gives an overview of their unfolding over time. Several citational databases must be searched to retrieve a complete set of studies despite the efforts to build repositories for COVID-19 literature. Our collected data can help build a more focused literature search between November 2019 and December 2021 when carrying out systematic and rapid reviews and our findings can give a complete picture on the topic.

Indexed as

COVID-19information retrievalopen scienceresearch practicescience communicationstext mining

Identifiers

PMID36325437
PMCPMC9619924

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.