Evidence map›Paper›PMID 32701448›Full record

ArticleeLife2020

The growth of acronyms in the scientific literature.

Adrian Barnett, Zoe Doubleday

Abstract read
In one paragraph

Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Review
  3. Personalized Jargon Identification for Enhanced Interdisciplinary Communication.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 2024
    Article
  4. Article
  5. Scientific novelty beyond the experiment.Microbial biotechnology · 2023
    Article
  6. Article
  7. Article
  8. PopScience: Teaching students to Communicate Scientific Findings to the General Public.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2023
    Article
  9. Article
  10. Article
  11. Be careful with abbreviations.Journal of hip preservation surgery · 2021
    Article
  12. Article
  13. Article
  14. Article
  15. 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

2 authors.

Adrian BarnettSchool of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.ORCID 0000-0001-6339-0374
Zoe DoubledayFuture Industries Institute, University of South Australia, Adelaide, Australia.ORCID 0000-0003-0045-6377

Funding

Australian Research Council FT190100244National Health and Medical Research Council APP1117784
6 · The paper itself

Abstract

Some acronyms are useful and are widely understood, but many of the acronyms used in scientific papers hinder understanding and contribute to the increasing fragmentation of science. Here we report the results of an analysis of more than 24 million article titles and 18 million article abstracts published between 1950 and 2019. There was at least one acronym in 19% of the titles and 73% of the abstracts. Acronym use has also increased over time, but the re-use of acronyms has declined. We found that from more than one million unique acronyms in our data, just over 2,000 (0.2%) were used regularly, and most acronyms (79%) appeared fewer than 10 times. Acronyms are not the biggest current problem in science communication, but reducing their use is a simple change that would help readers and potentially increase the value of science.

Indexed as

Abbreviations as TopicLiteratureEditorial PoliciesPeriodicals as TopicPublishingacronymscommunicationknowledgemeta-researchnonescientific publishingscientific writing

Identifiers

PMID32701448
PMCPMC7556863

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.