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Data-driven subtypes of major depressive disorder: a systematic review

Overview of attention for article published in BMC Medicine, December 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
13 tweeters
patent
1 patent

Citations

dimensions_citation
173 Dimensions

Readers on

mendeley
267 Mendeley
citeulike
2 CiteULike
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Title
Data-driven subtypes of major depressive disorder: a systematic review
Published in
BMC Medicine, December 2012
DOI 10.1186/1741-7015-10-156
Pubmed ID
Authors

Hanna M van Loo, Peter de Jonge, Jan-Willem Romeijn, Ronald C Kessler, Robert A Schoevers

Abstract

According to current classification systems, patients with major depressive disorder (MDD) may have very different combinations of symptoms. This symptomatic diversity hinders the progress of research into the causal mechanisms and treatment allocation. Theoretically founded subtypes of depression such as atypical, psychotic, and melancholic depression have limited clinical applicability. Data-driven analyses of symptom dimensions or subtypes of depression are scarce. In this systematic review, we examine the evidence for the existence of data-driven symptomatic subtypes of depression.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 267 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
United Kingdom 1 <1%
Australia 1 <1%
Chile 1 <1%
Belgium 1 <1%
Spain 1 <1%
Netherlands 1 <1%
Unknown 259 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 50 19%
Researcher 44 16%
Student > Master 40 15%
Student > Bachelor 28 10%
Other 17 6%
Other 60 22%
Unknown 28 10%
Readers by discipline Count As %
Psychology 65 24%
Medicine and Dentistry 57 21%
Neuroscience 24 9%
Agricultural and Biological Sciences 16 6%
Computer Science 13 5%
Other 45 17%
Unknown 47 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 23 April 2021.
All research outputs
#2,245,244
of 18,336,925 outputs
Outputs from BMC Medicine
#1,463
of 2,784 outputs
Outputs of similar age
#30,455
of 266,641 outputs
Outputs of similar age from BMC Medicine
#97
of 171 outputs
Altmetric has tracked 18,336,925 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,784 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 39.3. This one is in the 47th percentile – i.e., 47% 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 266,641 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 171 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.