Articles: cations.
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In the publication of this article [1], there was an error in a contributors Family Name. This has now been updated in the original article.
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We present in this paper the application of deep convolutional neural networks (CNNs), which is a state-of-the-art artificial intelligence (AI) approach in machine learning, for automated time-independent prediction of burn depth. Color images of four types of burn depth injured in first few days, including normal skin and background, acquired by a TiVi camera were trained and tested with four pretrained deep CNNs: VGG-16, GoogleNet, ResNet-50, and ResNet-101. ⋯ The accuracy was compared with the clinical diagnosis obtained after the wound had healed. Hence, application of AI is very promising for prediction of burn depth and, therefore, can be a useful tool to help in guiding clinical decision and initial treatment of burn wounds.
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Anesthesia & Analgesia (A&A) Practice is a journal for clinicians worldwide. It is aligned with the educational mission of its parent organization, the International Anesthesia Research Society. ⋯ A&A Practice seeks to publish short yet informative, peer-reviewed, PubMed indexed articles that offer a solution to a perioperative care or patient safety conundrum or a health management issue, which is communicated as one of the several manuscript types. We herein provide authors with a guide to assist them toward a successfully published manuscript in A&A Practice.
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The Publisher regrets that this article is an accidental duplication of an article that has already been published, https://doi.org/10.1016/j.jtcvs.2019.12.017. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal
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Patients are increasingly taking an active role in the design and delivery of surgical research. Public communication of results should also be encouraged, but this is often limited to non-expert commentary. This study assessed the role of plain English abstracts disseminated via social media in engaging patients and clinicians in the communication of surgical research. ⋯ Online, public engagement with surgical research was low. Overall engagement (predominantly from healthcare professionals) was enhanced by the use of visual abstracts.