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Mendeley readers
Attention Score in Context
| Title |
Selecting optimal screening items for delirium: an application of item response theory
|
|---|---|
| Published in |
BMC Medical Research Methodology, January 2013
|
| DOI | 10.1186/1471-2288-13-8 |
| Pubmed ID | |
| Authors |
Frances M Yang, Richard N Jones, Sharon K Inouye, Douglas Tommet, Paul K Crane, James L Rudolph, Long H Ngo, Edward R Marcantonio |
| Abstract |
Delirium (acute confusion), is a common, morbid, and costly complication of acute illness in older adults. Yet, researchers and clinicians lack short, efficient, and sensitive case identification tools for delirium. Though the Confusion Assessment Method (CAM) is the most widely used algorithm for delirium, the existing assessments that operationalize the CAM algorithm may be too long or complicated for routine clinical use. Item response theory (IRT) models help facilitate the development of short screening tools for use in clinical applications or research studies. This study utilizes IRT to identify a reduced set of optimally performing screening indicators for the four CAM features of delirium. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 4% |
| Unknown | 45 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 9 | 19% |
| Student > Doctoral Student | 8 | 17% |
| Researcher | 7 | 15% |
| Professor | 3 | 6% |
| Student > Postgraduate | 3 | 6% |
| Other | 10 | 21% |
| Unknown | 7 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 18 | 38% |
| Nursing and Health Professions | 6 | 13% |
| Social Sciences | 3 | 6% |
| Psychology | 3 | 6% |
| Business, Management and Accounting | 2 | 4% |
| Other | 5 | 11% |
| Unknown | 10 | 21% |
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 10 April 2023.
All research outputs
#15,853,124
of 23,556,846 outputs
Outputs from BMC Medical Research Methodology
#1,555
of 2,079 outputs
Outputs of similar age
#183,893
of 283,074 outputs
Outputs of similar age from BMC Medical Research Methodology
#23
of 28 outputs
Altmetric has tracked 23,556,846 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,079 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one is in the 16th percentile – i.e., 16% 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 283,074 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.