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CDEGenerator: an online platform to learn from existing data models to build model registries

Overview of attention for article published in Clinical Epidemiology, August 2018
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  • Average Attention Score compared to outputs of the same age

Mentioned by

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1 tweeter

Citations

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4 Dimensions

Readers on

mendeley
8 Mendeley
Title
CDEGenerator: an online platform to learn from existing data models to build model registries
Published in
Clinical Epidemiology, August 2018
DOI 10.2147/clep.s170075
Pubmed ID
Authors

Julian Varghese, Michael Fujarski, Stefan Hegselmann, Philipp Neuhaus, Martin Dugas

Abstract

Best-practice data models harmonize semantics and data structure of medical variables in clinical or epidemiological studies. While there exist several published data sets, it remains challenging to find and reuse published eligibility criteria or other data items that match specific needs of a newly planned study or registry. A novel Internet-based method for rapid comparison of published data models was implemented to enable reuse, customization, and harmonization of item catalogs for the early planning and development phase of research databases. Based on prior work, a European information infrastructure with a large collection of medical data models was established. A newly developed analysis module called CDEGenerator provides systematic comparison of selected data models and user-tailored creation of minimum data sets or harmonized item catalogs. Usability was assessed by eight external medical documentation experts in a workshop by the umbrella organization for networked medical research in Germany with the System Usability Scale. The analysis and item-tailoring module provides multilingual comparisons of semantically complex eligibility criteria of clinical trials. The System Usability Scale yielded "good usability" (mean 75.0, range 65.0-92.5). User-tailored models can be exported to several data formats, such as XLS, REDCap or Operational Data Model by the Clinical Data Interchange Standards Consortium, which is supported by the US Food and Drug Administration and European Medicines Agency for metadata exchange of clinical studies. The online tool provides user-friendly methods to reuse, compare, and thus learn from data items of standardized or published models to design a blueprint for a harmonized research database.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 25%
Student > Ph. D. Student 2 25%
Student > Bachelor 1 13%
Student > Master 1 13%
Student > Doctoral Student 1 13%
Other 0 0%
Unknown 1 13%
Readers by discipline Count As %
Computer Science 3 38%
Medicine and Dentistry 2 25%
Decision Sciences 1 13%
Engineering 1 13%
Unknown 1 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 26 October 2018.
All research outputs
#8,594,276
of 13,697,025 outputs
Outputs from Clinical Epidemiology
#271
of 429 outputs
Outputs of similar age
#161,153
of 268,200 outputs
Outputs of similar age from Clinical Epidemiology
#13
of 18 outputs
Altmetric has tracked 13,697,025 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 429 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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 268,200 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.