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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients
|
|---|---|
| Published in |
BMC Pulmonary Medicine, August 2012
|
| DOI | 10.1186/1471-2466-12-40 |
| Pubmed ID | |
| Authors |
Fabio S Aguiar, Luciana L Almeida, Antonio Ruffino-Netto, Afranio Lineu Kritski, Fernanda CQ Mello, Guilherme L Werneck |
| Abstract |
Tuberculosis (TB) remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission. |
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 % |
|---|---|---|
| Mexico | 1 | 50% |
| United States | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | 2% |
| United States | 2 | 2% |
| United Kingdom | 1 | <1% |
| Unknown | 124 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 27 | 21% |
| Student > Ph. D. Student | 14 | 11% |
| Student > Doctoral Student | 13 | 10% |
| Student > Master | 12 | 9% |
| Student > Postgraduate | 9 | 7% |
| Other | 26 | 20% |
| Unknown | 28 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 39 | 30% |
| Computer Science | 11 | 9% |
| Psychology | 6 | 5% |
| Mathematics | 4 | 3% |
| Social Sciences | 4 | 3% |
| Other | 25 | 19% |
| Unknown | 40 | 31% |
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 22 August 2012.
All research outputs
#15,248,503
of 22,673,450 outputs
Outputs from BMC Pulmonary Medicine
#1,069
of 1,892 outputs
Outputs of similar age
#106,141
of 166,600 outputs
Outputs of similar age from BMC Pulmonary Medicine
#16
of 26 outputs
Altmetric has tracked 22,673,450 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,892 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 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 166,600 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.