| Title |
Using Patients Like My Patient for Clinical Decision Support: Institution-Specific Probability of Celiac Disease Diagnosis Using Simplified Near-Neighbor Classification
|
|---|---|
| Published in |
Journal of General Internal Medicine, May 2013
|
| DOI | 10.1007/s11606-013-2443-z |
| Pubmed ID | |
| Authors |
Brian H. Shirts, Sterling T. Bennett, Brian R. Jackson |
| Abstract |
Interpretation of a diagnostic test result requires knowing what proportion of patients with a "similar" result has the condition in question. This information is often not readily available from the medical literature, or may be based on different clinical populations that make it nonapplicable. In certain settings, where correlated screening parameters and diagnostic data are available in electronic medical records, a representation of diagnostic test performance on "patients like my patient" can be obtained. |
X Demographics
The data shown below were collected from the profiles of 6 X users 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 | 17% |
| Unknown | 5 | 83% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 50% |
| Science communicators (journalists, bloggers, editors) | 2 | 33% |
| Scientists | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Unknown | 31 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 22% |
| Student > Master | 4 | 13% |
| Student > Ph. D. Student | 4 | 13% |
| Other | 3 | 9% |
| Student > Postgraduate | 3 | 9% |
| Other | 9 | 28% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 13 | 41% |
| Computer Science | 5 | 16% |
| Social Sciences | 4 | 13% |
| Agricultural and Biological Sciences | 1 | 3% |
| Psychology | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 5 | 16% |
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 November 2013.
All research outputs
#7,686,573
of 23,911,072 outputs
Outputs from Journal of General Internal Medicine
#4,140
of 7,806 outputs
Outputs of similar age
#63,714
of 195,231 outputs
Outputs of similar age from Journal of General Internal Medicine
#40
of 85 outputs
Altmetric has tracked 23,911,072 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,806 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.8. This one is in the 46th percentile – i.e., 46% 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 195,231 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 66% of its contemporaries.
We're also able to compare this research output to 85 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 51% of its contemporaries.