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X Demographics
Mendeley readers
Attention Score in Context
Title |
Developing model-based algorithms to identify screening colonoscopies using administrative health databases
|
---|---|
Published in |
BMC Medical Informatics and Decision Making, April 2013
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DOI | 10.1186/1472-6947-13-45 |
Pubmed ID | |
Authors |
Maida J Sewitch, Mengzhu Jiang, Lawrence Joseph, Robert J Hilsden, Alain Bitton |
Abstract |
Algorithms to identify screening colonoscopies in administrative databases would be useful for monitoring colorectal cancer (CRC) screening uptake, tracking health resource utilization, and quality assurance. Previously developed algorithms based on expert opinion were insufficiently accurate. The purpose of this study was to develop and evaluate the accuracy of model-based algorithms to identify screening colonoscopies in health administrative databases. |
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 % |
---|---|---|
India | 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 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 44 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 10 | 23% |
Student > Ph. D. Student | 9 | 20% |
Student > Master | 8 | 18% |
Student > Bachelor | 3 | 7% |
Student > Postgraduate | 2 | 5% |
Other | 5 | 11% |
Unknown | 7 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 17 | 39% |
Social Sciences | 3 | 7% |
Nursing and Health Professions | 3 | 7% |
Business, Management and Accounting | 2 | 5% |
Computer Science | 2 | 5% |
Other | 7 | 16% |
Unknown | 10 | 23% |
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 12 April 2013.
All research outputs
#18,335,133
of 22,705,019 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,563
of 1,981 outputs
Outputs of similar age
#151,085
of 199,476 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#35
of 38 outputs
Altmetric has tracked 22,705,019 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 1,981 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 9th percentile – i.e., 9% 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 199,476 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.