ReviewProceedings of the National Academy of Sciences of the United States of America2021
Analyzing the vast coronavirus literature with CoronaCentral.
Review in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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.
Who cites it
17 citing papers in PubMed.
- Coronavirus research topics, tracking twenty years of research.Scientific data · 2025Article
- PubMed and beyond: biomedical literature search in the age of artificial intelligence.EBioMedicine · 2024Review
- Article
- Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on COVID-19.Journal of medical Internet research · 2023Article
- Searching for evidence in public health emergencies: a white paper of best practices.Journal of the Medical Library Association : JMLA · 2023Article
- Comprehensively identifying Long Covid articles with human-in-the-loop machine learning.Patterns (New York, N.Y.) · 2023Article
- A survey on the role of artificial intelligence in managing Long COVID.Frontiers in artificial intelligence · 2023Review
- Optimal COVID-19 therapeutic candidate discovery using the CANDO platform.Frontiers in pharmacology · 2022Article
- Contribution of Deep-Learning Techniques Toward Fighting COVID-19: A Bibliometric Analysis of Scholarly Production During 2020.IEEE access : practical innovations, open solutions · 2022Article
- Proteases and variants: context matters for SARS-CoV-2 entry assays.Current opinion in virology · 2021Review
- Article
- An Open-Publishing Response to the COVID-19 Infodemic.CEUR workshop proceedings · 2021Article
- Individual COVID-19 disease trajectories revealed by plasma proteomics.EMBO molecular medicine · 2021Article
- Online Interactive Platform for COVID-19 Literature Visual Analytics: Platform Development Study.Journal of medical Internet research · 2021Article
- Challenges in defining Long COVID: Striking differences across literature, Electronic Health Records, and patient-reported information.medRxiv : the preprint server for health sciences · 2021Article
- Article
- The Asymmetric Influence of Emotion in the Sharing of COVID-19 Science on Social Media: Observational Study.JMIR infodemiologyArticle
Corrections and comments
- Update of
Authors and funding
2 authors.
Funding
Abstract
The SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming publication rate means that researchers are unable to keep abreast of the literature. To ameliorate this, we present the CoronaCentral resource that uses machine learning to process the research literature on SARS-CoV-2 together with SARS-CoV and MERS-CoV. We categorize the literature into useful topics and article types and enable analysis of the contents, pace, and emphasis of research during the crisis with integration of Altmetric data. These topics include therapeutics, disease forecasting, as well as growing areas such as "long COVID" and studies of inequality. This resource, available at https://coronacentral.ai, is updated daily.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.