Evidence map›Paper›PMID 35264375›Full record

ArticleBMJ health & care informatics2022

'Refbin' an online platform to extract and classify large-scale information: a pilot study of COVID-19 related papers.

Shania Lunna, Isabelle Flinn, James Prytherch, Camille Torfs-Leibman, Sarah Robtoy, Matt Bansak, David Krag

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

7 authors.

Shania LunnaUniversity of Vermont, Burlington, Vermont, USA.
Isabelle FlinnUniversity of Vermont, Burlington, Vermont, USA.
James PrytherchPlomics, Shelburne, Vermont, USA.
Camille Torfs-LeibmanUniversity of Vermont, Burlington, Vermont, USA.
Sarah RobtoyUniversity of Vermont, Burlington, Vermont, USA.
Matt BansakPlomics, Shelburne, Vermont, USA.
David KragSurgery, University of Vermont, Burlington, Vermont, USA david.krag@uvm.edu.ORCID http://orcid.org/0000-0001-5355-5999

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe number of new biomedical manuscripts published on important topics exceeds the capacity of single persons to read. Integration of literature is an even more elusive task. This article describes a pilot study of a scalable online system to integrate data from 1000 articles on COVID-19.

methodsArticles were imported from PubMed using the query 'COVID-19'. The full text of articles reporting new data was obtained and the results extracted manually. An online software system was used to enter the results. Similar results were bundled using note fields in parent-child order. Each extracted result was linked to the source article. Each new data entry comprised at least four note fields: (1) result, (2) population or sample, (3) description of the result and (4) topic. Articles underwent iterative rounds of group review over remote sessions.

resultsScreening 4126 COVID-19 articles resulted in a selection of 1000 publications presenting new data. The results were extracted and manually entered in note fields. Integration from multiple publications was achieved by sharing parent note fields by child entries. The total number of extracted primary results was 12 209. The mean number of results per article was 15.1 (SD 12.0). The average number of parent note fields for each result note field was 6.8 (SD 1.4). The total number of all note fields was 28 809. Without sharing of parent note fields, there would have been a total of 94 986 note fields.

conclusionThis pilot study demonstrates the feasibility of a scalable online system to extract results from 1000 manuscripts. Using four types of notes to describe each result provided standardisation of data entry and information integration. There was substantial reduction in complexity and reduction in total note fields by sharing of parent note fields. We conclude that this system provides a method to scale up extraction of information on very large topics.

Indexed as

COVID-19HumansPilot ProjectsResearch DesignSARS-CoV-2COVID-19data managementhealth information managementpublic healthsoftware

Identifiers

PMID35264375
PMCPMC8914395

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.