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A molecular computational model improves the preoperative diagnosis of thyroid nodules

Overview of attention for article published in BMC Cancer, September 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
2 blogs
twitter
4 tweeters

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
29 Mendeley
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Title
A molecular computational model improves the preoperative diagnosis of thyroid nodules
Published in
BMC Cancer, September 2012
DOI 10.1186/1471-2407-12-396
Pubmed ID
Authors

Sara Tomei, Ivo Marchetti, Katia Zavaglia, Francesca Lessi, Alessandro Apollo, Paolo Aretini, Giancarlo Di Coscio, Generoso Bevilacqua, Chiara Mazzanti

Abstract

Thyroid nodules with indeterminate cytological features on fine needle aspiration (FNA) cytology have a 20% risk of thyroid cancer. The aim of the current study was to determine the diagnostic utility of an 8-gene assay to distinguish benign from malignant thyroid neoplasm.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 38%
Other 4 14%
Student > Master 3 10%
Student > Ph. D. Student 2 7%
Student > Doctoral Student 2 7%
Other 5 17%
Unknown 2 7%
Readers by discipline Count As %
Medicine and Dentistry 14 48%
Biochemistry, Genetics and Molecular Biology 8 28%
Agricultural and Biological Sciences 4 14%
Unknown 3 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 02 October 2012.
All research outputs
#745,098
of 11,136,055 outputs
Outputs from BMC Cancer
#135
of 4,141 outputs
Outputs of similar age
#7,480
of 111,437 outputs
Outputs of similar age from BMC Cancer
#4
of 86 outputs
Altmetric has tracked 11,136,055 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,141 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 96% of its peers.
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 111,437 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 86 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.