Evidence map›Paper›PMID 36350613›Full record

ArticleNucleic acids research2023

LitCovid in 2022: an information resource for the COVID-19 literature.

Qingyu Chen, Alexis Allot, Robert Leaman, Chih-Hsuan Wei, Elaheh Aghaarabi, John J Guerrerio, Lilly Xu, Zhiyong Lu

Erratum issuedAbstract read
In one paragraph

Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

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

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. How the National Library of Medicine should evolve in an era of artificial intelligence.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Is metadata of articles about COVID-19 enough for multilabel topic classification task?Database : the journal of biological databases and curation · 2024
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Observational
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Qingyu ChenNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, USA.ORCID 0000-0002-6036-1516
Alexis AllotNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, USA.ORCID 0000-0002-2706-9054
Robert LeamanNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, USA.
Chih-Hsuan WeiNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, USA.
Elaheh AghaarabiNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, USA.
John J GuerrerioNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, USA.ORCID 0000-0001-6547-9137
Lilly XuNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, USA.
Zhiyong LuNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, USA.ORCID 0000-0001-9998-916X

Funding

Machine Learning and Natural Language Processing for Biomedical ApplicationsZIALM237241 · NLM · NATIONAL LIBRARY OF MEDICINE · PI LU, ZHIYONG · 2023 to 2025
$12.2M
6 · The paper itself

Abstract

LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/)-first launched in February 2020-is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19. The number of articles in LitCovid has increased from 55 000 to ∼300 000 over the past 2.5 years, with a consistent growth rate of ∼10 000 articles per month. In addition to the rapid literature growth, the COVID-19 pandemic has evolved dramatically. For instance, the Omicron variant has now accounted for over 98% of new infections in the United States. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last 2 years. First, we introduced the long Covid collection consisting of the articles on COVID-19 survivors experiencing ongoing multisystemic symptoms, including respiratory issues, cardiovascular disease, cognitive impairment, and profound fatigue. Second, we provided new annotations on the latest COVID-19 strains and vaccines mentioned in the literature. Third, we improved several existing features with more accurate machine learning algorithms for annotating topics and classifying articles relevant to COVID-19. LitCovid has been widely used with millions of accesses by users worldwide on various information needs and continues to play a critical role in collecting, curating and standardizing the latest knowledge on the COVID-19 literature.

Indexed as

COVID-19Databases, BibliographicHumansPandemicsPost-Acute COVID-19 SyndromeSARS-CoV-2United States

Identifiers

PMID36350613
PMCPMC9825538

What Socratic holds

Textmetadata
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 Socratic graph.