M
Matthew Roman
Researcher at Duke University
Publications - 10
Citations - 1198
Matthew Roman is an academic researcher from Duke University. The author has contributed to research in topics: Myelopathy & Back pain. The author has an hindex of 8, co-authored 9 publications receiving 663 citations. Previous affiliations of Matthew Roman include Durham University.
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Journal ArticleDOI
Telehealth transformation: COVID-19 and the rise of virtual care.
Jedrek Wosik,Marat Fudim,Blake Cameron,Ziad F. Gellad,Ziad F. Gellad,Alex Cho,Donna Phinney,Simon Curtis,Matthew Roman,Matthew Roman,Eric G. Poon,Jeffrey M. Ferranti,Jeffrey M. Ferranti,Jason N. Katz,James E. Tcheng +14 more
TL;DR: The role that telehealth has played in transforming healthcare delivery during the 3 phases of the U.S. COVID-19 pandemic is described and how people, process, and technology work together to support a successful telehealth transformation is examined.
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Reliability and Diagnostic Accuracy of Clinical Special Tests for Myelopathy in Patients Seen for Cervical Dysfunction
TL;DR: This study demonstrated that 4 of 7 tests used to screen for myelopathy offered substantial levels of interrater agreement when used on individuals with cervical dysfunction, and the Babinski sign did alter posttest probability more significantly than combinations of test findings.
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Clustered clinical findings for diagnosis of cervical spine myelopathy
TL;DR: Clustered combinations of clinical findings that could rule in and rule out CSM are found and may be useful in identifying patients with this complex diagnosis in similar patient populations.
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The clinical value of a cluster of patient history and observational findings as a diagnostic support tool for lumbar spine stenosis.
Chad Cook,Chris Brown,Keith W. Michael,Robert E. Isaacs,Cameron Howes,William J. Richardson,Matthew Roman,Eric J. Hegedus +7 more
TL;DR: The high sensitivity of the diagnostic support tool provides the potential to reduce the incidence of unnecessary imaging when the diagnosis of LSS is statistically unlikely, with a potential to increase diagnostic efficiency and reduce cost by narrowing the indications for imaging.
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The development of a clinical decision making algorithm for detection of osteoporotic vertebral compression fracture or wedge deformity
TL;DR: In this paper, the diagnosis of OVCF was made after assessment of radiographic findings in sagittal alignment, vertebral body compression, and spinal canal dimensions, and the most diagnostic combination included a cluster of age > 52 years, no presence of leg pain; body mass index ⩽ 22; (4) does not exercise regularly; and (5) female gender.