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Silvio E. Inzucchi

Researcher at Yale University

Publications -  511
Citations -  64889

Silvio E. Inzucchi is an academic researcher from Yale University. The author has contributed to research in topics: Diabetes mellitus & Type 2 diabetes. The author has an hindex of 92, co-authored 424 publications receiving 54101 citations. Previous affiliations of Silvio E. Inzucchi include University of Toronto & American Diabetes Association.

Papers
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Journal ArticleDOI

Diagnosis and management of pituitary adenomas.

Ranee R. Lleva, +1 more
- 01 Jan 2011 - 
TL;DR: Advances in medical management for each pituitary adenomas are discussed, highlighting new therapeutic alternatives and the role of surgery is also reviewed.
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Understanding Contemporary Use of Thiazolidinediones An Analysis From the Diabetes Collaborative Registry

Suzanne V. Arnold, +6 more
- 01 Jun 2019 - 
TL;DR: The oral insulin-sensitizing medications used in type 2 diabetes mellitus that reduce glucose with minimal risk of hypoglycemia and pote... as discussed by the authors, are:
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Treatment of diabetes in the elderly. Addressing its complexities in this high-risk group.

Olga V. Sakharova, +1 more
- 01 Nov 2005 - 
TL;DR: An overview of therapy is presented, discussing the antidiabetic agents available and their best use in the older population with diabetes.
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Glargine and lispro: two cases of mistaken identity.

Marcy A. Adlersberg, +3 more
- 01 Feb 2002 - 
TL;DR: Insulin glargine (Lantus) is a recently available basal insulin analog that appears to have a more consistent activity profile than comparable long-acting insulin products and may be particularly useful in individuals who demonstrate labile blood glucose control with conventional insulin formulations.
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Empagliflozin and uric acid metabolism in diabetes: A post hoc analysis of the EMPA-REG OUTCOME trial

João Pedro Ferreira, +8 more
- 04 Oct 2021 - 
TL;DR: In this article, the effect of empagliflozin on uric acid (UA) levels, antigout medication and gout episodes in the EMPA-REG OUTCOME trial was evaluated using mixed linear models.