Publications by authors named "Kathrin Patzer"

The use of perioperative antibiotic prophylaxis in cutaneous surgery is controversial due to unclear efficacy and, thus, potentially unnecessary side-effects. This prospective observational study analysed the efficacy of oral perioperative antibiotic prophylaxis in preventing surgical site infections. Adult patients undergoing cutaneous surgery between August 2020 and May 2021 at Ludwig-Maximilian University Hospital Munich, Germany, without prior signs of infection were eligible.

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Background: Surgical site infection (SSI) has a significant impact on patients' morbidity and aesthetic results.

Objective: To identify risk factors for SSI in dermatologic surgery.

Patients And Methods: This prospective, single-centre, observational study was performed between August 2020 and May 2021.

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Background: Identifying risk factors is essential for preventing surgical site infections (SSIs) in dermatologic surgery.

Objective: To analyze whether specific procedure-related factors are associated with SSI.

Methods: This systematic review of the literature included MEDLINE, EMBASE, CENTRAL, and trial registers.

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Postoperative wound infection in dermatologic surgery causes impaired wound healing, poor cosmetic outcome and increased morbidity. Patients with a high-risk profile may benefit from perioperative antibiotic prophylaxis. The objective of this systematic review was to identify risk factors for surgical site infection after dermatologic surgery.

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Introduction: Identifying risk factors for wound infection may guide clinical practice for optimal use of perioperative antibiotic prophylaxis in dermatologic surgery.

Objective: To summarize the current evidence whether specific body sites have higher risks for surgical site infections (SSI).

Methods: The systematic literature search included MEDLINE, Embase, CENTRAL, and trial registers.

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Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology.

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