Evidence map›Paper›PMID 36351703›Full record

ArticleBMJ health & care informatics2022

Pilot trial comparing COVID-19 publication database to conventional online search methods.

Camille Torfs-Leibman, Takamaru Ashikaga, David Krag, Shania Lunna, Sarah Robtoy, Rachel Bombardier

Abstract read
In one paragraph

Article in BMJ health & care informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Camille Torfs-LeibmanUniversity of Vermont, Burlington, Vermont, USA ctorfsleibman@gmail.com.
Takamaru AshikagaDepartment of Biomedical Statistics, University of Vermont, Burlington, Vermont, USA.
David KragDepartment of Surgery, University of Vermont, Burlington, Vermont, USA.ORCID http://orcid.org/0000-0001-5355-5999
Shania LunnaDepartment of Surgery, University of Vermont, Burlington, Vermont, USA.
Sarah RobtoyUniversity of Vermont, Burlington, Vermont, USA.
Rachel BombardierUniversity of Vermont Larner College of Medicine, Burlington, Vermont, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesLiterature review using search engines results in a list of manuscripts but does not provide the content contained in the manuscripts. Our goal was to evaluate user performance-based criteria of concept retrieval accuracy and efficiency using a new database system that contained information extracted from 1000 COVID-19 articles.

methodsA sample of 17 students from the University of Vermont were randomly assigned to use the COVID-19 publication database or their usual preferred search methods to research eight prompts about COVID-19. The relevance and accuracy of the evidence found for each prompt were graded. A Cox proportional hazards' model with a sandwich estimator and Kaplan-Meier plots were used to analyse these data in a time-to-correct answer context.

resultsOur findings indicate that students using the new information management system answered significantly more prompts correctly and, in less time, than students using conventional research methods. Bivariate models for demographic factors indicated that previous research experience conferred an advantage in study performance, though it was found to be independent from the assigned research method.

conclusionsThe results from this pilot randomised trial present a potential tool for more quickly and thoroughly navigating the literature on expansive topics such as COVID-19.

Indexed as

COVID-19HumansOnline SystemsPilot ProjectsInformation ManagementOnline Systems

Identifiers

PMID36351703
PMCPMC9644076

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LicenceCC BY-NC
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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.