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Customization in prescribing for bipolar disorder

Overview of attention for article published in Health economics (Online), April 2011
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

blogs
1 blog

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mendeley
52 Mendeley
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Article details
Title
Customization in prescribing for bipolar disorder
Published in
Health economics (Online), April 2011
DOI 10.1002/hec.1737
Pubmed ID
Authors
Abstract

For many disorders, patient heterogeneity requires physicians to customize their treatment to each patient's needs. We test for the existence of customization in physicians' prescribing for bipolar disorder, using data from a naturalistic clinical effectiveness trial of bipolar disorder treatment (STEP-BD), which did not constrain physician prescribing. Multinomial logit is used to model the physician's choice among five combinations of drug classes. We find that our observed measure of the patient's clinical status played only a limited role in the choice among drug class combinations, even for conditions such as mania that are expected to affect class choice. However, treatment of a patient with given characteristics differed widely depending on which physician was seen. The explanatory power of the model was low. There was variation within each physician's prescribing, but the results do not suggest a high degree of customization in physicians' prescribing, based on our measure of clinical status.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 2%
Latvia 1 2%
Unknown 50 96%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 13 25%
Student > Ph. D. Student 6 12%
Student > Doctoral Student 5 10%
Student > Bachelor 5 10%
Other 4 8%
Other 8 15%
Unknown 11 21%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 11 21%
Psychology 7 13%
Economics, Econometrics and Finance 6 12%
Mathematics 3 6%
Social Sciences 3 6%
Other 10 19%
Unknown 12 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 April 2012.
All research outputs
#9,607,477
of 34,400,738 outputs
Outputs from Health economics (Online)
#1,612
of 2,847 outputs
Outputs of similar age
#52,623
of 161,450 outputs
Outputs of similar age from Health economics (Online)
#30
of 47 outputs
Altmetric has tracked 34,400,738 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 2,847 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.9. This one is in the 41st percentile – i.e., 41% 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 161,450 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 65% of its contemporaries.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.