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Generating and evaluating evidence of the clinical utility of molecular diagnostic tests in oncology

Overview of attention for article published in Genetics in Medicine, December 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Average Attention Score compared to outputs of the same age and source

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11 X users
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2 Facebook pages

Citations

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19 Dimensions

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41 Mendeley
Title
Generating and evaluating evidence of the clinical utility of molecular diagnostic tests in oncology
Published in
Genetics in Medicine, December 2015
DOI 10.1038/gim.2015.162
Pubmed ID
Authors

Patricia Deverka, Donna A. Messner, Robert McCormack, Gary H. Lyman, Margaret Piper, Linda Bradley, David Parkinson, David Nelson, Mary Lou Smith, Louis Jacques, Tania Dutta, Sean R. Tunis

Abstract

Enthusiasm for molecular diagnostic (MDx) testing in oncology is constrained by the gaps in required evidence regarding its impact on patient outcomes (clinical utility (CU)). This effectiveness guidance document proposes recommendations for the design and evaluation of studies intended to reflect the evidence expectations of payers, while also reflecting information needs of patients and clinicians. Our process included literature reviews and key informant interviews followed by iterative virtual and in-person consultation with an expert technical working group and an advisory group comprising life-sciences industry experts, public and private payers, patients, clinicians, regulators, researchers, and other stakeholders. Treatment decisions in oncology represent high-risk clinical decision making, and therefore the recommendations give preference to randomized controlled trials (RCTs) for demonstrating CU. The guidance also describes circumstances under which alternatives to RCTs could be considered, specifying conditions under which test developers could use prospective-retrospective studies with banked biospecimens, single-arm studies, prospective observational studies, or decision-analytic modeling techniques that make a reasonable case for CU. Using a process driven by multiple stakeholders, we developed a common framework for designing and evaluating studies of the clinical validity and CU of MDx tests, achieving a balance between internal validity of the studies and the relevance, feasibility, and timeliness of generating the desired evidence.Genet Med advance online publication 03 December 2015Genetics in Medicine (2015); doi:10.1038/gim.2015.162.

X Demographics

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The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 40 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 20%
Other 5 12%
Student > Doctoral Student 3 7%
Student > Master 3 7%
Professor 2 5%
Other 3 7%
Unknown 17 41%
Readers by discipline Count As %
Medicine and Dentistry 9 22%
Agricultural and Biological Sciences 4 10%
Biochemistry, Genetics and Molecular Biology 4 10%
Social Sciences 3 7%
Physics and Astronomy 1 2%
Other 3 7%
Unknown 17 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 13 January 2016.
All research outputs
#5,309,230
of 25,374,647 outputs
Outputs from Genetics in Medicine
#1,502
of 2,943 outputs
Outputs of similar age
#81,160
of 395,183 outputs
Outputs of similar age from Genetics in Medicine
#23
of 48 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,943 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.0. This one is in the 48th percentile – i.e., 48% 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 395,183 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 79% of its contemporaries.
We're also able to compare this research output to 48 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 50% of its contemporaries.