Evidence map›Paper›PMID 33916878›Full record

ArticleAntibiotics (Basel, Switzerland)2021

Finding What Is Inaccessible: Antimicrobial Resistance Language Use among the One Health Domains.

Lauren L Wind, Jonathan S Briganti, Anne M Brown, Timothy P Neher, Meghan F Davis, Lisa M Durso, Tanner Spicer, Stephanie Lansing

Abstract read
In one paragraph

Article in Antibiotics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. 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

8 authors.

Lauren L WindDepartment of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24060, USA.ORCID 0000-0002-0805-1086
Jonathan S BrigantiUniversity Libraries, Virginia Tech, Blacksburg, VA 24060, USA.
Anne M BrownUniversity Libraries, Virginia Tech, Blacksburg, VA 24060, USA.ORCID 0000-0001-6951-8228
Timothy P NeherDepartment of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, USA.ORCID 0000-0001-6935-4032
Meghan F DavisJohns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Lisa M DursoUSDA-ARS, Lincoln, NE 68583, USA.ORCID 0000-0002-9702-6463
Tanner SpicerUniversity Libraries, Virginia Tech, Blacksburg, VA 24060, USA.
Stephanie LansingDepartment of Environmental Science and Technology, University of Maryland, College Park, MD 20742, USA.ORCID 0000-0001-9169-3823

Funding

USDA NIFA 2018-68003-27467USDA NIFA 2019-67017-29114
6 · The paper itself

Abstract

The success of a One Health approach to combating antimicrobial resistance (AMR) requires effective data sharing across the three One Health domains (human, animal, and environment). To investigate if there are differences in language use across the One Health domains, we examined the peer-reviewed literature using a combination of text data mining and natural language processing techniques on 20,000 open-access articles related to AMR and One Health. Evaluating AMR key term frequency from the European PubMed Collection published between 1990 and 2019 showed distinct AMR language usage within each domain and incongruent language usage across domains, with significant differences in key term usage frequencies when articles were grouped by the One Health sub-specialties (2-way ANOVA;

Indexed as

AMRanimalantibiotic resistanceantimicrobial resistanceARcommon languageenvironmenthumannatural language processingone healthtext data mining

Identifiers

PMID33916878
PMCPMC8065768

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.