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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, March 2012
|
| DOI | 10.1186/1472-6947-12-19 |
| Pubmed ID | |
| Authors |
Michael Marschollek, Mehmet Gövercin, Stefan Rust, Matthias Gietzelt, Mareike Schulze, Klaus-Hendrik Wolf, Elisabeth Steinhagen-Thiessen |
| Abstract |
Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). |
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 % |
|---|---|---|
| United States | 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 106 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 1 | <1% |
| Germany | 1 | <1% |
| Switzerland | 1 | <1% |
| United Kingdom | 1 | <1% |
| Canada | 1 | <1% |
| Unknown | 101 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 20% |
| Researcher | 14 | 13% |
| Student > Master | 14 | 13% |
| Other | 13 | 12% |
| Student > Bachelor | 10 | 9% |
| Other | 15 | 14% |
| Unknown | 19 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 28 | 26% |
| Nursing and Health Professions | 14 | 13% |
| Computer Science | 14 | 13% |
| Engineering | 7 | 7% |
| Social Sciences | 4 | 4% |
| Other | 11 | 10% |
| Unknown | 28 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 07 May 2020.
All research outputs
#7,170,310
of 22,663,969 outputs
Outputs from BMC Medical Informatics and Decision Making
#718
of 1,978 outputs
Outputs of similar age
#49,640
of 156,709 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#11
of 29 outputs
Altmetric has tracked 22,663,969 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 1,978 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 62% of its peers.
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 156,709 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.