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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Utilizing machine learning dimensionality reduction for risk stratification of chest pain patients in the emergency department
|
|---|---|
| Published in |
BMC Medical Research Methodology, April 2021
|
| DOI | 10.1186/s12874-021-01265-2 |
| Pubmed ID | |
| Authors |
Nan Liu, Marcel Lucas Chee, Zhi Xiong Koh, Su Li Leow, Andrew Fu Wah Ho, Dagang Guo, Marcus Eng Hock Ong |
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 % |
|---|---|---|
| Singapore | 1 | 50% |
| Italy | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 50 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 7 | 14% |
| Researcher | 4 | 8% |
| Student > Doctoral Student | 4 | 8% |
| Student > Ph. D. Student | 3 | 6% |
| Student > Master | 3 | 6% |
| Other | 12 | 24% |
| Unknown | 17 | 34% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 8 | 16% |
| Computer Science | 6 | 12% |
| Medicine and Dentistry | 6 | 12% |
| Engineering | 4 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 4% |
| Other | 8 | 16% |
| Unknown | 16 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 11 June 2022.
All research outputs
#3,642,513
of 22,653,392 outputs
Outputs from BMC Medical Research Methodology
#565
of 2,000 outputs
Outputs of similar age
#90,111
of 430,121 outputs
Outputs of similar age from BMC Medical Research Methodology
#20
of 56 outputs
Altmetric has tracked 22,653,392 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,000 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has gotten more attention than average, scoring higher than 71% 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 430,121 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 56 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 64% of its contemporaries.