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Mendeley readers
Attention Score in Context
| Title |
Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis
|
|---|---|
| Published in |
Critical Care, February 2010
|
| DOI | 10.1186/cc8864 |
| Pubmed ID | |
| Authors |
Mitchell J Cohen, Adam D Grossman, Diane Morabito, M Margaret Knudson, Atul J Butte, Geoffrey T Manley |
| Abstract |
Advances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome. |
Mendeley readers
The data shown below were compiled from readership statistics for 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Germany | 1 | <1% |
| Italy | 1 | <1% |
| France | 1 | <1% |
| Denmark | 1 | <1% |
| United Kingdom | 1 | <1% |
| Unknown | 114 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 28 | 23% |
| Student > Ph. D. Student | 26 | 21% |
| Professor | 13 | 11% |
| Professor > Associate Professor | 11 | 9% |
| Student > Bachelor | 7 | 6% |
| Other | 24 | 20% |
| Unknown | 12 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 39 | 32% |
| Computer Science | 21 | 17% |
| Engineering | 13 | 11% |
| Agricultural and Biological Sciences | 10 | 8% |
| Neuroscience | 5 | 4% |
| Other | 20 | 17% |
| Unknown | 13 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 16 November 2017.
All research outputs
#8,535,472
of 25,374,917 outputs
Outputs from Critical Care
#4,397
of 6,554 outputs
Outputs of similar age
#51,250
of 172,638 outputs
Outputs of similar age from Critical Care
#23
of 54 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,554 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.8. This one is in the 28th percentile – i.e., 28% 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 172,638 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.