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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Estimating the optimal threshold for a diagnostic biomarker in case of complex biomarker distributions
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, June 2014
|
| DOI | 10.1186/1472-6947-14-53 |
| Pubmed ID | |
| Authors |
Fabien Subtil, Muriel Rabilloud |
| Abstract |
Estimating the optimal threshold (and especially the confidence interval) of a quantitative biomarker to be used as a diagnostic test is essential for medical decision-making. This is often done with simple methods that are not always reliable. More advanced methods work well but only for biomarkers with very simple distributions. In fact, biomarker distributions are often complex because of a natural heterogeneity in marker expression and other heterogeneities due to various disease stages, laboratory equipments, etc. Methods are required to estimate a biomarker optimal threshold in case of heterogeneity and complex distributions. |
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 % |
|---|---|---|
| India | 1 | 50% |
| United Kingdom | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 43 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 21% |
| Researcher | 9 | 21% |
| Student > Master | 6 | 14% |
| Student > Postgraduate | 3 | 7% |
| Other | 3 | 7% |
| Other | 6 | 14% |
| Unknown | 7 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 5 | 12% |
| Medicine and Dentistry | 5 | 12% |
| Mathematics | 5 | 12% |
| Computer Science | 4 | 9% |
| Biochemistry, Genetics and Molecular Biology | 3 | 7% |
| Other | 12 | 28% |
| Unknown | 9 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 20 September 2018.
All research outputs
#6,778,566
of 22,757,090 outputs
Outputs from BMC Medical Informatics and Decision Making
#654
of 1,985 outputs
Outputs of similar age
#64,759
of 228,190 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#8
of 25 outputs
Altmetric has tracked 22,757,090 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 66% 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 228,190 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 25 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.