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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computational modeling of peripheral pain: a commentary
|
|---|---|
| Published in |
BioMedical Engineering OnLine, June 2015
|
| DOI | 10.1186/s12938-015-0049-x |
| Pubmed ID | |
| Authors |
Erick J Argüello, Ricardo J Silva, Mónica K Huerta, René S Avila |
| Abstract |
This commentary is intended to find possible explanations for the low impact of computational modeling on pain research. We discuss the main strategies that have been used in building computational models for the study of pain. The analysis suggests that traditional models lack biological plausibility at some levels, they do not provide clinically relevant results, and they cannot capture the stochastic character of neural dynamics. On this basis, we provide some suggestions that may be useful in building computational models of pain with a wider range of applications. |
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 % |
|---|---|---|
| Japan | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 2% |
| Unknown | 53 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 28% |
| Researcher | 9 | 17% |
| Student > Master | 7 | 13% |
| Professor | 6 | 11% |
| Other | 3 | 6% |
| Other | 4 | 7% |
| Unknown | 10 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 13 | 24% |
| Agricultural and Biological Sciences | 7 | 13% |
| Neuroscience | 7 | 13% |
| Computer Science | 3 | 6% |
| Chemistry | 3 | 6% |
| Other | 13 | 24% |
| Unknown | 8 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 27 November 2020.
All research outputs
#14,228,602
of 22,811,321 outputs
Outputs from BioMedical Engineering OnLine
#371
of 824 outputs
Outputs of similar age
#138,243
of 266,811 outputs
Outputs of similar age from BioMedical Engineering OnLine
#11
of 18 outputs
Altmetric has tracked 22,811,321 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 824 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 52% 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 266,811 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.