You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
Mendeley readers
Attention Score in Context
| Title |
Bayesian Estimation of Mixture Models with Prespecified Elements to Compare Drug Resistance in Treatment-Naïve and Experienced Tuberculosis Cases
|
|---|---|
| Published in |
PLoS Computational Biology, March 2013
|
| DOI | 10.1371/journal.pcbi.1002973 |
| Pubmed ID | |
| Authors |
Alane Izu, Ted Cohen, Victor DeGruttola |
| Abstract |
We propose a Bayesian approach for estimating branching tree mixture models to compare drug-resistance pathways (i.e. patterns of sequential acquisition of resistance to individual antibiotics) that are observed among Mycobacterium tuberculosis isolates collected from treatment-naïve and treatment-experienced patients. Resistant pathogens collected from treatment-naïve patients are strains for which fitness costs of resistance were not sufficient to prevent transmission, whereas those collected from treatment-experienced patients reflect both transmitted and acquired resistance, the latter of which may or may not be associated with lower transmissibility. The comparison of the resistance pathways constructed from these two groups of drug-resistant strains provides insight into which pathways preferentially lead to the development of multiple drug resistant strains that are transmissible. We apply the proposed statistical methods to data from worldwide surveillance of drug-resistant tuberculosis collected by the World Health Organization over 13 years. |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 60 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 9 | 15% |
| Researcher | 8 | 13% |
| Student > Postgraduate | 6 | 10% |
| Student > Doctoral Student | 5 | 8% |
| Student > Bachelor | 4 | 6% |
| Other | 16 | 26% |
| Unknown | 14 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 14 | 23% |
| Agricultural and Biological Sciences | 7 | 11% |
| Immunology and Microbiology | 4 | 6% |
| Nursing and Health Professions | 3 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 3% |
| Other | 9 | 15% |
| Unknown | 23 | 37% |
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 March 2013.
All research outputs
#22,778,604
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#8,570
of 8,964 outputs
Outputs of similar age
#185,078
of 210,459 outputs
Outputs of similar age from PLoS Computational Biology
#138
of 152 outputs
Altmetric has tracked 25,394,764 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 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. 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 210,459 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 152 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.