Publications by authors named "C M J Jacobs"

Background: Pathologic confirmation of lung cancer influences treatment selection for suspected early-stage lung cancer. High pre-treatment tissue confirmation rates are recommended. We sought to define management and outcomes of patients undergoing surgery for primary lung cancer in a UK multi-centre clinical trial.

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Occasioned by the publication of , the text represents exchanges held in the summer of 2024 at the invitation of . It addresses the genesis and the history of the project, and its impact on psychoanalytic rethinking, formation, and practice. While exploring the potential and the limits of revisioning, it also raises questions about the nature of transmission of psychoanalytic knowledge and about the field's relation to the state of its own standards in the age of Anthropocene.

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Erythrodermic psoriasis (EP) is a rare and challenging-to-diagnose severe form of psoriasis. Its presentation can have similarities with other inflammatory skin conditions, complicating subsequent management. We present a case of a 76-year-old woman with EP who presented with fever, tachycardia, leg swelling with pain and redness, and reduced consciousness.

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Article Synopsis
  • AI and mixed reality (MR) are changing healthcare by improving patient care and medical education through technology and human-computer interaction (HCI).
  • AI-driven virtual therapists offer personalized, remote mental health support, while MR allows patients to confront fears in safe, virtual environments.
  • Clinical training benefits from adaptive, immersive tools that provide hands-on practice in high-pressure scenarios, emphasizing the need for ethical considerations like data security as these technologies become more integrated into healthcare.
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Artificial Intelligence (AI) models may fail or suffer from reduced performance when applied to unseen data that differs from the training data distribution, referred to as dataset shift. Automatic detection of out-of-distribution (OOD) data contributes to safe and reliable clinical implementation of AI models. In this study, we propose a recognized OOD detection method that utilizes the Mahalanobis distance (MD) and compare its performance to widely known classical methods.

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