ArticleJournal of the American Medical Informatics Association : JAMIA2020
TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- SMARTEN: a human-AI hybrid framework for assisted medical literature analysis and its evaluation.BMC medical education · 2026Article
- Retrieval augmented generation for large language models in healthcare: A systematic review.PLOS digital health · 2025Article
- BioASQ Synergy: a dialogue between question-answering systems and biomedical experts for promoting COVID-19 research.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Retrieval augmented scientific claim verification.JAMIA open · 2024Article
- A pipeline for the retrieval and extraction of domain-specific information with application to COVID-19 immune signatures.BMC bioinformatics · 2023Article
- Can the crowd judge truthfulness? A longitudinal study on recent misinformation about COVID-19.Personal and ubiquitous computing · 2023Article
- A COVID-19 Search Engine (CO-SE) with Transformer-based architecture.Healthcare analytics (New York, N.Y.) · 2022Article
- Understanding the genetics of viral drug resistance by integrating clinical data and mining of the scientific literature.Scientific reports · 2022Article
- GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings.Information sciences · 2022Article
- Multi-probe attention neural network for COVID-19 semantic indexing.BMC bioinformatics · 2022Article
- Information retrieval and question answering: A case study on COVID-19 scientific literature.Knowledge-based systems · 2022Article
- Search like an expert: Reducing expertise disparity using a hybrid neural index for COVID-19 queries.Journal of biomedical informatics · 2022Article
- Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies.Briefings in bioinformatics · 2022Review
- A term-based and citation network-based search system for COVID-19.JAMIA open · 2021Article
- Information Retrieval in an Infodemic: The Case of COVID-19 Publications.Journal of medical Internet research · 2021Article
- Searching for scientific evidence in a pandemic: An overview of TREC-COVID.Journal of biomedical informatics · 2021Review
- Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World.IEEE internet of things journal · 2021Article
- Developing a sampling method and preliminary taxonomy for classifying COVID-19 public health guidance for healthcare organizations and the general public.Journal of biomedical informatics · 2021Article
- Analyzing the vast coronavirus literature with CoronaCentral.Proceedings of the National Academy of Sciences of the United States of America · 2021Review
- A comparative analysis of system features used in the TREC-COVID information retrieval challenge.Journal of biomedical informatics · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
TREC-COVID is an information retrieval (IR) shared task initiated to support clinicians and clinical research during the COVID-19 pandemic. IR for pandemics breaks many normal assumptions, which can be seen by examining 9 important basic IR research questions related to pandemic situations. TREC-COVID differs from traditional IR shared task evaluations with special considerations for the expected users, IR modality considerations, topic development, participant requirements, assessment process, relevance criteria, evaluation metrics, iteration process, projected timeline, and the implications of data use as a post-task test collection. This article describes how all these were addressed for the particular requirements of developing IR systems under a pandemic situation. Finally, initial participation numbers are also provided, which demonstrate the tremendous interest the IR community has in this effort.
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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.