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Georg Osterhoff

Researcher at Leipzig University

Publications -  181
Citations -  3097

Georg Osterhoff is an academic researcher from Leipzig University. The author has contributed to research in topics: Medicine & Fracture fixation. The author has an hindex of 24, co-authored 151 publications receiving 2095 citations. Previous affiliations of Georg Osterhoff include University of Zurich & University of British Columbia.

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Early computed tomography or focused assessment with sonography in abdominal trauma: what are the leading opinions?

TL;DR: It seems that the results of recent studies supporting early WBCT have not yet found broad acceptance in the surgical community, whereas FAST is performed with similar frequency and is prioritized in unstable patients.
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Bone quality measured by the radiogrammetric parameter “cortical index” and reoperations after locking plate osteosynthesis in patients sustaining proximal humerus fractures

TL;DR: The risk for reoperation is independent of the CI even though the CI may be a predictor for proximal humerus fracture, and younger patients should be aware that surgical treatment of proximal Humerus fractures might be a two-stage surgery.
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A Cost-Effectiveness Analysis of Reverse Total Shoulder Arthroplasty versus Hemiarthroplasty for the Management of Complex Proximal Humeral Fractures in the Elderly

TL;DR: The economic analysis found that RTSA for the treatment of complex proximal humeral fractures in the elderly is the preferred economic strategy when compared with HA, and its estimate of cost-effectiveness is similar to other highly successful orthopedic strategies such as total hip arthroplasty for thetreatment of hip arthritis.
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Cement Augmentation in Sacroiliac Screw Fixation Offers Modest Biomechanical Advantages in a Cadaver Model

TL;DR: The addition of cement to standard sacroiliac screw fixation seemed to change the mode and dynamics of failure in this cadaveric mechanical model.
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Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning.

TL;DR: In this article, the authors analyzed the PPG- and rPPG-based BP prediction error with respect to the underlying data distribution, which revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures.