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From Principle to Practice: Bridging the Gap in Patient Profiling

Overview of attention for article published in PLOS ONE, January 2013
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1 X user
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2 Facebook pages

Citations

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10 Dimensions

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43 Mendeley
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Title
From Principle to Practice: Bridging the Gap in Patient Profiling
Published in
PLOS ONE, January 2013
DOI 10.1371/journal.pone.0054728
Pubmed ID
Authors

Jonathan H. Foley, Thomas Orfeo, Anetta Undas, Kelley C. McLean, Ira M. Bernstein, Georges-Etienne Rivard, Kenneth G. Mann, Stephen J. Everse, Kathleen E. Brummel-Ziedins

Abstract

The standard clinical coagulation assays, activated partial thromboplastin time (aPTT) and prothrombin time (PT) cannot predict thrombotic or bleeding risk. Since thrombin generation is central to haemorrhage control and when unregulated, is the likely cause of thrombosis, thrombin generation assays (TGA) have gained acceptance as "global assays" of haemostasis. These assays generate an enormous amount of data including four key thrombin parameters (lag time, maximum rate, peak and total thrombin) that may change to varying degrees over time in longitudinal studies. Currently, each thrombin parameter is averaged and presented individually in a table, bar graph or box plot; no method exists to visualize comprehensive thrombin generation data over time. To address this need, we have created a method that visualizes all four thrombin parameters simultaneously and can be animated to evaluate how thrombin generation changes over time. This method uses all thrombin parameters to intrinsically rank individuals based on their haemostatic status. The thrombin generation parameters can be derived empirically using TGA or simulated using computational models (CM). To establish the utility and diverse applicability of our method we demonstrate how warfarin therapy (CM), factor VIII prophylaxis for haemophilia A (CM), and pregnancy (TGA) affects thrombin generation over time. The method is especially suited to evaluate an individual's thrombotic and bleeding risk during "normal" processes (e.g pregnancy or aging) or during therapeutic challenges to the haemostatic system. Ultimately, our method is designed to visualize individualized patient profiles which are becoming evermore important as personalized medicine strategies become routine clinical practice.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
Slovenia 1 2%
Unknown 41 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 23%
Student > Ph. D. Student 8 19%
Student > Master 5 12%
Student > Bachelor 3 7%
Lecturer 2 5%
Other 6 14%
Unknown 9 21%
Readers by discipline Count As %
Medicine and Dentistry 14 33%
Agricultural and Biological Sciences 4 9%
Pharmacology, Toxicology and Pharmaceutical Science 3 7%
Mathematics 3 7%
Nursing and Health Professions 2 5%
Other 7 16%
Unknown 10 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 31 January 2013.
All research outputs
#14,160,293
of 22,693,205 outputs
Outputs from PLOS ONE
#115,757
of 193,724 outputs
Outputs of similar age
#166,604
of 280,879 outputs
Outputs of similar age from PLOS ONE
#2,834
of 5,029 outputs
Altmetric has tracked 22,693,205 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 193,724 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one is in the 36th percentile – i.e., 36% 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 280,879 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5,029 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.