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Jedrek Wosik

Researcher at University of Texas Southwestern Medical Center

Publications -  11
Citations -  127

Jedrek Wosik is an academic researcher from University of Texas Southwestern Medical Center. The author has contributed to research in topics: Overfitting & Deep learning. The author has an hindex of 5, co-authored 11 publications receiving 101 citations. Previous affiliations of Jedrek Wosik include University of Texas at Dallas & Duke University.

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Journal Article

Impact of a Disposable Sterile Radiation Shield on Operator Radiation Exposure During Percutaneous Coronary Intervention of Chronic Total Occlusions.

TL;DR: Compared with non-CTO PCI, patients undergoing CTO-PCI were more likely to have congestive heart failure, to be current smokers, and to have longer lesions, and lesslikely to have prior PCI and a saphenous vein graft target lesion.
Journal ArticleDOI

Impact of a disposable sterile radiation shield on operator radiation exposure during percutaneous coronary intervention of chronic total occlusions

TL;DR: In this article, the use of a disposable radiation protection sterile drape (Radpad; Worldwide Innovations & Technologies, Inc) was used in all CTO-PCI cases vs none of the non-CTO PCI cases.
Journal ArticleDOI

Premature coronary heart disease and autosomal dominant hypercholesterolemia: Increased risk in women with LDLR mutations.

TL;DR: The findings suggest that genetic confirmation of ADH may be important to identify patient's risk of CHD, especially for female LDLR mutation carriers.
Journal Article

Systematic Review of the BridgePoint System for Crossing Coronary and Peripheral Chronic Total Occlusions.

TL;DR: The BridgePoint system is associated with high procedural success rates and low complication rates when used for crossing both coronary and peripheral CTOs.

Students Need More Attention: BERT-based AttentionModel for Small Data with Application to AutomaticPatient Message Triage

TL;DR: A novel framework based on BioBERT (Bidirectional Encoder Representations from Transformers for biomedical TextMining) is proposed, which is utilized to build a model for patient portal message triage that classifies the urgency of a message into three categories: non-urgent, medium and urgent.