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Prediction of opioid dose in cancer pain patients using genetic profiling: not yet an option with support vector machine learning

Overview of attention for article published in BMC Research Notes, January 2018
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Title
Prediction of opioid dose in cancer pain patients using genetic profiling: not yet an option with support vector machine learning
Published in
BMC Research Notes, January 2018
DOI 10.1186/s13104-018-3194-z
Pubmed ID
Authors

Anne Estrup Olesen, Debbie Grønlund, Mikkel Gram, Frank Skorpen, Asbjørn Mohr Drewes, Pål Klepstad

Abstract

Use of opioids for pain management has increased over the past decade; however, inadequate analgesic response is common. Genetic variability may be related to opioid efficacy, but due to the many possible combinations and variables, statistical computations may be difficult. This study investigated whether data processing with support vector machine learning could predict required opioid dose in cancer pain patients, using genetic profiling. Eighteen single nucleotide polymorphisms (SNPs) within the µ and δ opioid receptor genes and the catechol-O-methyltransferase gene were selected for analysis. Data from 1237 cancer pain patients were included in the analysis. Support vector machine learning did not find any associations between the assessed SNPs and opioid dose in cancer pain patients, and hence, did not provide additional information regarding prediction of required opioid dose using genetic profiling.

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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Professor 3 20%
Student > Ph. D. Student 3 20%
Student > Master 3 20%
Researcher 2 13%
Professor > Associate Professor 1 7%
Other 1 7%
Unknown 2 13%
Readers by discipline Count As %
Medicine and Dentistry 6 40%
Computer Science 1 7%
Nursing and Health Professions 1 7%
Biochemistry, Genetics and Molecular Biology 1 7%
Neuroscience 1 7%
Other 1 7%
Unknown 4 27%

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 30 January 2018.
All research outputs
#9,958,158
of 12,439,436 outputs
Outputs from BMC Research Notes
#1,932
of 2,780 outputs
Outputs of similar age
#247,487
of 341,198 outputs
Outputs of similar age from BMC Research Notes
#31
of 42 outputs
Altmetric has tracked 12,439,436 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,780 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
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We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.