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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
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 % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 2% |
| United States | 1 | 2% |
| Sweden | 1 | 2% |
| Unknown | 55 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 19% |
| Researcher | 9 | 16% |
| Student > Master | 7 | 12% |
| Student > Postgraduate | 6 | 10% |
| Other | 5 | 9% |
| Other | 10 | 17% |
| Unknown | 10 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 19 | 33% |
| Engineering | 8 | 14% |
| Business, Management and Accounting | 3 | 5% |
| Computer Science | 3 | 5% |
| Agricultural and Biological Sciences | 3 | 5% |
| Other | 7 | 12% |
| Unknown | 15 | 26% |
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
#17,728,987
of 22,766,595 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,498
of 1,984 outputs
Outputs of similar age
#172,228
of 255,754 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#19
of 21 outputs
Altmetric has tracked 22,766,595 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 1,984 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 255,754 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% 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 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.