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Acute Invasive Fungal Rhinosinusitis: A Comprehensive Update of CT Findings and Design of an Effective Diagnostic Imaging Model

Overview of attention for article published in American Journal of Neuroradiology, April 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

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26 X users

Citations

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101 Dimensions

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90 Mendeley
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Title
Acute Invasive Fungal Rhinosinusitis: A Comprehensive Update of CT Findings and Design of an Effective Diagnostic Imaging Model
Published in
American Journal of Neuroradiology, April 2015
DOI 10.3174/ajnr.a4298
Pubmed ID
Authors

E.H. Middlebrooks, C.J. Frost, R.O. De Jesus, T.C. Massini, I.M. Schmalfuss, A.A. Mancuso

Abstract

Acute invasive fungal rhinosinusitis carries a high mortality rate. An easy-to-use and accurate predictive imaging model is currently lacking. We assessed the performance of various CT findings for the identification of acute invasive fungal rhinosinusitis and synthesized a simple and robust diagnostic model to serve as an easily applicable screening tool for at-risk patients. Two blinded neuroradiologists retrospectively graded 23 prespecified imaging abnormalities in the craniofacial region on craniofacial CT examinations from 42 patients with pathology-proven acute invasive fungal rhinosinusitis and 42 control patients proved negative for acute invasive fungal rhinosinusitis from the same high-risk population. A third blinded neuroradiologist decided discrepancies. Specificity, sensitivity, positive predictive value, and negative predictive value were determined for all individual variables. The 23 variables were evaluated for intercorrelations and univariate correlations and were interrogated by using stepwise linear regression. Given the low predictive value of any individual variable, a 7-variable model (periantral fat, bone dehiscence, orbital invasion, septal ulceration, pterygopalatine fossa, nasolacrimal duct, and lacrimal sac) was synthesized on the basis of multivariate analysis. The presence of abnormality involving a single variable in the model has an 87% positive predictive value, 95% negative predictive value, 95% sensitivity, and 86% specificity (R(2) = 0.661). A positive outcome in any 2 of the model variables predicted acute invasive fungal rhinosinusitis with 100% specificity and 100% positive predictive value. Our 7-variable CT-based model provides an easily applicable and robust screening tool to triage patients at risk for acute invasive fungal rhinosinusitis into a disease-positive or -negative category with a high degree of confidence.

X Demographics

X Demographics

The data shown below were collected from the profiles of 26 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
Brazil 1 1%
Unknown 88 98%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 15 17%
Researcher 13 14%
Other 10 11%
Student > Doctoral Student 10 11%
Student > Bachelor 6 7%
Other 20 22%
Unknown 16 18%
Readers by discipline Count As %
Medicine and Dentistry 56 62%
Agricultural and Biological Sciences 3 3%
Pharmacology, Toxicology and Pharmaceutical Science 2 2%
Immunology and Microbiology 2 2%
Nursing and Health Professions 2 2%
Other 6 7%
Unknown 19 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 15 September 2019.
All research outputs
#1,828,492
of 22,828,180 outputs
Outputs from American Journal of Neuroradiology
#294
of 4,879 outputs
Outputs of similar age
#23,054
of 237,835 outputs
Outputs of similar age from American Journal of Neuroradiology
#1
of 76 outputs
Altmetric has tracked 22,828,180 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,879 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done particularly well, scoring higher than 93% 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 237,835 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 90% of its contemporaries.
We're also able to compare this research output to 76 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 98% of its contemporaries.