You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
Title |
A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay
|
---|---|
Published in |
BMC Medical Informatics and Decision Making, May 2010
|
DOI | 10.1186/1472-6947-10-27 |
Pubmed ID | |
Authors |
Andrew A Kramer, Jack E Zimmerman |
Abstract |
Patients with a prolonged intensive care unit (ICU) length of stay account for a disproportionate amount of resource use. Early identification of patients at risk for a prolonged length of stay can lead to quality enhancements that reduce ICU stay. This study developed and validated a model that identifies patients at risk for a prolonged ICU stay. |
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.
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 1 | <1% |
Italy | 1 | <1% |
Ghana | 1 | <1% |
Brazil | 1 | <1% |
Argentina | 1 | <1% |
United States | 1 | <1% |
Unknown | 111 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 24 | 21% |
Student > Ph. D. Student | 17 | 15% |
Researcher | 16 | 14% |
Other | 9 | 8% |
Professor | 7 | 6% |
Other | 19 | 16% |
Unknown | 25 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 42 | 36% |
Computer Science | 9 | 8% |
Nursing and Health Professions | 8 | 7% |
Engineering | 7 | 6% |
Business, Management and Accounting | 5 | 4% |
Other | 13 | 11% |
Unknown | 33 | 28% |
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 08 May 2014.
All research outputs
#17,687,135
of 22,708,120 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,495
of 1,981 outputs
Outputs of similar age
#84,976
of 94,976 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#7
of 10 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,981 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% 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 94,976 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.