| Title |
Too Many Definitions of Sepsis: Can Machine Learning Leverage the Electronic Health Record to Increase Accuracy and Bring Consensus?
|
|---|---|
| Published in |
Critical Care Medicine, February 2020
|
| DOI | 10.1097/ccm.0000000000004144 |
| Pubmed ID | |
| Authors |
Suchi Saria, Katharine E. Henry |
X Demographics
The data shown below were collected from the profiles of 37 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 States | 10 | 27% |
| Spain | 2 | 5% |
| Malaysia | 1 | 3% |
| Germany | 1 | 3% |
| Colombia | 1 | 3% |
| Portugal | 1 | 3% |
| United Kingdom | 1 | 3% |
| Kenya | 1 | 3% |
| France | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 18 | 49% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 20 | 54% |
| Scientists | 8 | 22% |
| Practitioners (doctors, other healthcare professionals) | 7 | 19% |
| Science communicators (journalists, bloggers, editors) | 2 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 62 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 11% |
| Unspecified | 6 | 10% |
| Other | 6 | 10% |
| Student > Ph. D. Student | 5 | 8% |
| Student > Doctoral Student | 4 | 6% |
| Other | 12 | 19% |
| Unknown | 22 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 18% |
| Computer Science | 9 | 15% |
| Unspecified | 6 | 10% |
| Engineering | 3 | 5% |
| Nursing and Health Professions | 2 | 3% |
| Other | 9 | 15% |
| Unknown | 22 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 20. 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 21 October 2022.
All research outputs
#1,860,610
of 25,387,668 outputs
Outputs from Critical Care Medicine
#1,240
of 9,342 outputs
Outputs of similar age
#45,426
of 470,256 outputs
Outputs of similar age from Critical Care Medicine
#38
of 162 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,342 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.8. This one has done well, scoring higher than 86% 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 470,256 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 162 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.