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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Bias in clinical chemistry
|
|---|---|
| Published in |
Bioanalysis, November 2014
|
| DOI | 10.4155/bio.14.249 |
| Pubmed ID | |
| URN |
urn:nbn:se:liu:diva-113794
|
| Authors |
Elvar Theodorsson, Bertil Magnusson, Ivo Leito |
| Abstract |
Clinical chemistry uses automated measurement techniques and medical knowledge in the interest of patients and healthy subjects. Automation has reduced repeatability and day-to-day variation considerably. Bias has been reduced to a lesser extent by reference measurement systems. It is vital to minimize clinically important bias, in particular bias within conglomerates of laboratories that measure samples from the same patients. Small and variable bias components will over time show random error properties and conventional random-error based methods for calculating measurement uncertainty can then be applied. The present overview of bias presents the general principles of error and uncertainty concepts, terminology and analysis, and suggests methods to minimize bias and measurement uncertainty in the interest of healthcare. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Serbia | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 112 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 19% |
| Student > Master | 16 | 14% |
| Student > Ph. D. Student | 9 | 8% |
| Other | 7 | 6% |
| Professor | 7 | 6% |
| Other | 25 | 22% |
| Unknown | 27 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 19 | 17% |
| Biochemistry, Genetics and Molecular Biology | 15 | 13% |
| Chemistry | 13 | 12% |
| Agricultural and Biological Sciences | 6 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 5% |
| Other | 19 | 17% |
| Unknown | 34 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 16 December 2014.
All research outputs
#15,312,760
of 22,774,233 outputs
Outputs from Bioanalysis
#739
of 1,307 outputs
Outputs of similar age
#151,720
of 260,565 outputs
Outputs of similar age from Bioanalysis
#15
of 45 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,307 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
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 260,565 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
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 57% of its contemporaries.