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Diagnostic heterogeneity in psychiatry: towards an empirical solution

Overview of attention for article published in BMC Medicine, September 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

news
3 news outlets
blogs
1 blog
twitter
2 tweeters

Citations

dimensions_citation
55 Dimensions

Readers on

mendeley
121 Mendeley
citeulike
1 CiteULike
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Title
Diagnostic heterogeneity in psychiatry: towards an empirical solution
Published in
BMC Medicine, September 2013
DOI 10.1186/1741-7015-11-201
Pubmed ID
Authors

Klaas J Wardenaar, Peter de Jonge

Abstract

The launch of the 5th version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) has sparked a debate about the current approach to psychiatric classification. The most basic and enduring problem of the DSM is that its classifications are heterogeneous clinical descriptions rather than valid diagnoses, which hampers scientific progress. Therefore, more homogeneous evidence-based diagnostic entities should be developed. To this end, data-driven techniques, such as latent class- and factor analyses, have already been widely applied. However, these techniques are insufficient to account for all relevant levels of heterogeneity, among real-life individuals. There is heterogeneity across persons (p:for example, subgroups), across symptoms (s:for example, symptom dimensions) and over time (t:for example, course-trajectories) and these cannot be regarded separately. Psychiatry should upgrade to techniques that can analyze multi-mode (p-by-s-by-t) data and can incorporate all of these levels at the same time to identify optimal homogeneous subgroups (for example, groups with similar profiles/connectivity of symptomatology and similar course). For these purposes, Multimode Principal Component Analysis and (Mixture)-Graphical Modeling may be promising techniques.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters 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 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Switzerland 1 <1%
Netherlands 1 <1%
South Africa 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 115 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 26%
Researcher 17 14%
Student > Master 16 13%
Student > Doctoral Student 9 7%
Student > Bachelor 9 7%
Other 21 17%
Unknown 17 14%
Readers by discipline Count As %
Psychology 41 34%
Medicine and Dentistry 22 18%
Neuroscience 11 9%
Computer Science 4 3%
Engineering 4 3%
Other 17 14%
Unknown 22 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 35. 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 24 November 2019.
All research outputs
#966,522
of 22,721,584 outputs
Outputs from BMC Medicine
#688
of 3,410 outputs
Outputs of similar age
#9,172
of 198,485 outputs
Outputs of similar age from BMC Medicine
#20
of 59 outputs
Altmetric has tracked 22,721,584 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,410 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.5. This one has done well, scoring higher than 79% 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 198,485 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.