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X Demographics
Mendeley readers
Attention Score in Context
| Title |
High performance implementation of the hierarchical likelihood for generalized linear mixed models: an application to estimate the potassium reference range in massive electronic health records datasets
|
|---|---|
| Published in |
BMC Medical Research Methodology, July 2021
|
| DOI | 10.1186/s12874-021-01318-6 |
| Pubmed ID | |
| Authors |
Cristian G. Bologa, Vernon Shane Pankratz, Mark L. Unruh, Maria Eleni Roumelioti, Vallabh Shah, Saeed Kamran Shaffi, Soraya Arzhan, John Cook, Christos Argyropoulos |
X Demographics
The data shown below were collected from the profiles of 22 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 | 4 | 18% |
| Canada | 2 | 9% |
| Spain | 1 | 5% |
| Australia | 1 | 5% |
| Malaysia | 1 | 5% |
| United Kingdom | 1 | 5% |
| Switzerland | 1 | 5% |
| Curaçao | 1 | 5% |
| Unknown | 10 | 45% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 14 | 64% |
| Scientists | 4 | 18% |
| Practitioners (doctors, other healthcare professionals) | 3 | 14% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 17 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 24% |
| Student > Ph. D. Student | 2 | 12% |
| Student > Doctoral Student | 1 | 6% |
| Unknown | 10 | 59% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 2 | 12% |
| Nursing and Health Professions | 2 | 12% |
| Computer Science | 1 | 6% |
| Medicine and Dentistry | 1 | 6% |
| Unknown | 11 | 65% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 09 December 2023.
All research outputs
#3,778,608
of 25,830,005 outputs
Outputs from BMC Medical Research Methodology
#580
of 2,324 outputs
Outputs of similar age
#84,864
of 446,282 outputs
Outputs of similar age from BMC Medical Research Methodology
#13
of 45 outputs
Altmetric has tracked 25,830,005 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,324 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has gotten more attention than average, scoring higher than 74% 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 446,282 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 80% of its contemporaries.
We're also able to compare this research output to 45 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 68% of its contemporaries.