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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Detecting contaminated birthdates using generalized additive models
|
|---|---|
| Published in |
BMC Bioinformatics, June 2014
|
| DOI | 10.1186/1471-2105-15-185 |
| Pubmed ID | |
| Authors |
Wei Luo, Marcus Gallagher, Bill Loveday, Susan Ballantyne, Jason P Connor, Janet Wiles |
| Abstract |
Erroneous patient birthdates are common in health databases. Detection of these errors usually involves manual verification, which can be resource intensive and impractical. By identifying a frequent manifestation of birthdate errors, this paper presents a principled and statistically driven procedure to identify erroneous patient birthdates. |
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 % |
|---|---|---|
| Norway | 1 | 50% |
| Unknown | 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 18 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor | 3 | 17% |
| Researcher | 3 | 17% |
| Student > Ph. D. Student | 3 | 17% |
| Student > Bachelor | 2 | 11% |
| Student > Doctoral Student | 1 | 6% |
| Other | 3 | 17% |
| Unknown | 3 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 22% |
| Engineering | 2 | 11% |
| Medicine and Dentistry | 2 | 11% |
| Mathematics | 1 | 6% |
| Decision Sciences | 1 | 6% |
| Other | 3 | 17% |
| Unknown | 5 | 28% |
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 13 June 2014.
All research outputs
#18,373,576
of 22,757,090 outputs
Outputs from BMC Bioinformatics
#6,305
of 7,272 outputs
Outputs of similar age
#164,093
of 228,693 outputs
Outputs of similar age from BMC Bioinformatics
#117
of 153 outputs
Altmetric has tracked 22,757,090 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,272 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 228,693 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 153 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.