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Mendeley readers
Attention Score in Context
| Title |
Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions
|
|---|---|
| Published in |
BMC Public Health, March 2012
|
| DOI | 10.1186/1471-2458-12-166 |
| Pubmed ID | |
| Authors |
Geneviève Cadieux, David L Buckeridge, André Jacques, Michael Libman, Nandini Dendukuri, Robyn Tamblyn |
| Abstract |
Syndromic surveillance systems are plagued by high false-positive rates. In chronic disease monitoring, investigators have identified several factors that predict the accuracy of case definitions based on diagnoses in administrative data, and some have even incorporated these predictors into novel case detection methods, resulting in a significant improvement in case definition accuracy. Based on findings from these studies, we sought to identify physician, patient, encounter, and billing characteristics associated with the positive predictive value (PPV) of case definitions for 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory (including influenza-like illness)). |
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 % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| Canada | 1 | 2% |
| Switzerland | 1 | 2% |
| Australia | 1 | 2% |
| Unknown | 42 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 20% |
| Student > Ph. D. Student | 7 | 15% |
| Student > Master | 5 | 11% |
| Student > Bachelor | 4 | 9% |
| Professor | 3 | 7% |
| Other | 9 | 20% |
| Unknown | 9 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 24% |
| Agricultural and Biological Sciences | 6 | 13% |
| Computer Science | 4 | 9% |
| Social Sciences | 3 | 7% |
| Biochemistry, Genetics and Molecular Biology | 2 | 4% |
| Other | 10 | 22% |
| Unknown | 10 | 22% |
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 04 April 2012.
All research outputs
#20,156,138
of 22,663,969 outputs
Outputs from BMC Public Health
#13,786
of 14,743 outputs
Outputs of similar age
#141,559
of 156,267 outputs
Outputs of similar age from BMC Public Health
#185
of 193 outputs
Altmetric has tracked 22,663,969 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,743 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one is in the 1st percentile – i.e., 1% 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 156,267 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 193 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.