Evidence map›Paper›PMID 35039972›Full record

ArticleMedicine, health care, and philosophy2022

Making Biomedical Sciences publications more accessible for machines.

Joris Van Meenen, Hanne Leysen, Hongyu Chen, Rudi Baccarne, Deborah Walter, Bronwen Martin, Stuart Maudsley

Abstract read
PubMed Publisher
In one paragraph

Article in Medicine, health care, and philosophy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Medicine and machines.Medicine, health care, and philosophy · 2022
    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.

Joris Van MeenenReceptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium.
Hanne LeysenReceptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium.
Hongyu ChenWeill Cornell Medical College, New York, NY, USA.
Rudi BaccarneAnet Library Automation, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium.
Deborah WalterReceptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium.
Bronwen MartinFaculty of Pharmaceutical, Veterinary and Biomedical Sciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium.
Stuart MaudsleyReceptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium. stuart.maudsley@uantwerpen.be.ORCID http://orcid.org/0000-0002-1868-184X

Funding

Fonds Wetenschappelijk Onderzoek 1198020NFonds Wetenschappelijk Onderzoek 42/FA010100/32/6484
6 · The paper itself

Abstract

With the rapidly expanding catalogue of scientific publications, especially within the Biomedical Sciences field, it is becoming increasingly difficult for researchers to search for, read or even interpret emerging scientific findings. PubMed, just one of the current biomedical data repositories, comprises over 33 million citations for biomedical research, and over 2500 publications are added each day. To further strengthen the impact biomedical research, we suggest that there should be more synergy between publications and machines. By bringing machines into the realm of research and publication, we can greatly augment the assessment, investigation and cataloging of the biomedical literary corpus. The effective application of machine-based manuscript assessment and interpretation is now crucial, and potentially stands as the most effective way for researchers to comprehend and process the tsunami of biomedical data and literature. Many biomedical manuscripts are currently published online in poorly searchable document types, with figures and data presented in formats that are partially inaccessible to machine-based approaches. The structure and format of biomedical manuscripts should be adapted to facilitate machine-assisted interrogation of this important literary corpus. In this context, it is important to embrace the concept that biomedical scientists should also write manuscripts that can be read by machines. It is likely that an enhanced human-machine synergy in reading biomedical publications will greatly enhance biomedical data retrieval and reveal novel insights into complex datasets.

Indexed as

Biomedical ResearchInformation Storage and RetrievalHumansPublicationsInteroperabilityMachineOpen accessReproducibilityResearch

Identifiers

What OpenQuestion 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 OpenQuestion graph.