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A straightforward approach to designing a scoring system for predicting length-of-stay of cardiac surgery patients

Overview of attention for article published in BMC Medical Informatics and Decision Making, October 2014
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  • Average Attention Score compared to outputs of the same age

Mentioned by

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1 tweeter
facebook
1 Facebook page

Citations

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4 Dimensions

Readers on

mendeley
37 Mendeley
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Title
A straightforward approach to designing a scoring system for predicting length-of-stay of cardiac surgery patients
Published in
BMC Medical Informatics and Decision Making, October 2014
DOI 10.1186/1472-6947-14-89
Pubmed ID
Authors

Paolo Barbini, Emanuela Barbini, Simone Furini, Gabriele Cevenini

Abstract

Length-of-stay prediction for cardiac surgery patients is a key point for medical management issues, such as optimization of resources in intensive care units and operating room scheduling. Scoring systems are a very attractive family of predictive models, but their retraining and updating are generally critical. The present approach to designing a scoring system for predicting length of stay in intensive care aims to overcome these difficulties, so that a model designed in a given scenario can easily be adjusted over time or for internal purposes.

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

Geographical breakdown

Country Count As %
India 1 3%
United States 1 3%
Sweden 1 3%
Unknown 34 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 22%
Researcher 8 22%
Student > Postgraduate 5 14%
Other 4 11%
Student > Bachelor 3 8%
Other 7 19%
Unknown 2 5%
Readers by discipline Count As %
Medicine and Dentistry 13 35%
Engineering 5 14%
Business, Management and Accounting 4 11%
Agricultural and Biological Sciences 3 8%
Computer Science 2 5%
Other 5 14%
Unknown 5 14%

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 15 October 2014.
All research outputs
#2,877,816
of 4,507,778 outputs
Outputs from BMC Medical Informatics and Decision Making
#594
of 754 outputs
Outputs of similar age
#73,836
of 121,063 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#18
of 21 outputs
Altmetric has tracked 4,507,778 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 754 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 18th percentile – i.e., 18% 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 121,063 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.