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Mendeley readers
Attention Score in Context
| Title |
A simulation model of colorectal cancer surveillance and recurrence
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, April 2014
|
| DOI | 10.1186/1472-6947-14-29 |
| Pubmed ID | |
| Authors |
Johnie Rose, Knut Magne Augestad, Chung Yin Kong, Neal J Meropol, Michael W Kattan, Qingqing Hong, Xuebei An, Gregory S Cooper |
| Abstract |
Approximately one-third of those treated curatively for colorectal cancer (CRC) will experience recurrence. No evidence-based consensus exists on how best to follow patients after initial treatment to detect asymptomatic recurrence. Here, a new approach for simulating surveillance and recurrence among CRC survivors is outlined, and development and calibration of a simple model applying this approach is described. The model's ability to predict outcomes for a group of patients under a specified surveillance strategy is validated. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 33% |
| United Kingdom | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 33% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Bangladesh | 1 | 2% |
| Austria | 1 | 2% |
| Unknown | 49 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 24% |
| Researcher | 10 | 20% |
| Student > Bachelor | 4 | 8% |
| Student > Doctoral Student | 3 | 6% |
| Other | 3 | 6% |
| Other | 11 | 22% |
| Unknown | 8 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 26 | 51% |
| Engineering | 3 | 6% |
| Agricultural and Biological Sciences | 2 | 4% |
| Unspecified | 1 | 2% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 2% |
| Other | 7 | 14% |
| Unknown | 11 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 17 January 2017.
All research outputs
#14,779,591
of 22,753,345 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,226
of 1,985 outputs
Outputs of similar age
#129,006
of 228,038 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#22
of 28 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 34th percentile – i.e., 34% 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 228,038 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.