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Medical 3D printing: methods to standardize terminology and report trends

Overview of attention for article published in 3D Printing in Medicine, March 2017
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  • Among the highest-scoring outputs from this source (#41 of 111)
  • Above-average Attention Score compared to outputs of the same age (62nd percentile)

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1 policy source
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Title
Medical 3D printing: methods to standardize terminology and report trends
Published in
3D Printing in Medicine, March 2017
DOI 10.1186/s41205-017-0012-5
Pubmed ID
Authors

Leonid Chepelev, Andreas Giannopoulos, Anji Tang, Dimitrios Mitsouras, Frank J. Rybicki

Abstract

Medical 3D printing is expanding exponentially, with tremendous potential yet to be realized in nearly all facets of medicine. Unfortunately, multiple informal subdomain-specific isolated terminological 'silos' where disparate terminology is used for similar concepts are also arising as rapidly. It is imperative to formalize the foundational terminology at this early stage to facilitate future knowledge integration, collaborative research, and appropriate reimbursement. The purpose of this work is to develop objective, literature-based consensus-building methodology for the medical 3D printing domain to support expert consensus. We first quantitatively survey the temporal, conceptual, and geographic diversity of all existing published applications within medical 3D printing literature and establish the existence of self-isolating research clusters. We then demonstrate an automated objective methodology to aid in establishing a terminological consensus for the field based on objective analysis of the existing literature. The resultant analysis provides a rich overview of the 3D printing literature, including publication statistics and trends globally, chronologically, technologically, and within each major medical discipline. The proposed methodology is used to objectively establish the dominance of the term "3D printing" to represent a collection of technologies that produce physical models in the medical setting. We demonstrate that specific domains do not use this term in line with objective consensus and call for its universal adoption. Our methodology can be applied to the entirety of medical 3D printing literature to obtain a complete, validated, and objective set of recommended and synonymous definitions to aid expert bodies in building ontological consensus.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 90 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 15 17%
Student > Ph. D. Student 14 16%
Researcher 10 11%
Student > Doctoral Student 7 8%
Professor 5 6%
Other 17 19%
Unknown 22 24%
Readers by discipline Count As %
Engineering 23 26%
Medicine and Dentistry 19 21%
Materials Science 3 3%
Social Sciences 3 3%
Physics and Astronomy 3 3%
Other 13 14%
Unknown 26 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 April 2019.
All research outputs
#6,989,215
of 22,919,505 outputs
Outputs from 3D Printing in Medicine
#41
of 111 outputs
Outputs of similar age
#121,203
of 333,606 outputs
Outputs of similar age from 3D Printing in Medicine
#1
of 3 outputs
Altmetric has tracked 22,919,505 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 111 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.5. This one has gotten more attention than average, scoring higher than 63% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 333,606 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them