Patients with advanced illness such as advanced stage cancer presenting with the need for possible surgical intervention can be some of the most challenging cases for a surgeon. Often there are multiple factors influencing the decisions made. For patients they are facing not just the effects of the disease on their body, but the stark realization that the disease will also limit their life. Not only are these factors a consideration when patients are making decisions, but also the desire to make the decision that is best for themselves, the autonomous decision. Also included in this process for the patient facing the possible need for an intervention is the surgeon. While patient autonomy remains one of the main principles within medicine, guiding treatment decisions, there is also the surgeon's autonomy to be considered. Surgeons determine if there is even a possible intervention to be offered to patients, a decision making process that respects surgeons' autonomous choices and includes elements of paternalism as surgeons utilize their expertise to make decisions. Included in the treatment decisions that are made and the care of the patient is the impact patients' outcomes have on the surgeon, the inherent drive to be the best for the patient and desire for good outcomes for the patient. While both the patient's and surgeon's autonomy are a dynamic interface influencing decision making, the main goal for the patient facing a palliative procedure is that of making treatment decisions based on the concept of shared decision making, always giving primary consideration to the patient's goals and values. Lastly, regardless of the decision made, it is the responsibility of surgeons to their patients to be a source of support through this challenging time.
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http://dx.doi.org/10.3978/j.issn.2305-5839.2016.01.36 | DOI Listing |
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Midwifery Department, Faculty and Health Sciences, Karabük University, Turkey.
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Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
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December 2024
School of Healthcare, University of Leicester, Leicester LE1 7RH, UK.
Vaccinating care home staff is essential to protect vulnerable residents by reducing infection risks and creating a safer care environment. However, vaccine hesitancy amongst staff remains a challenge, particularly since the COVID-19 pandemic raised concerns about side effects and vaccination mandates. This study examines how the pandemic influenced flu vaccine hesitancy amongst UK care home staff.
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December 2024
The Shamir Medical Center (Assaf Harofeh), Rishon LeZion, 4 Icet, Zrifin 7033001, Israel.
In response to the COVID-19 pandemic, Israel prioritized pregnant women for vaccination, recognizing them as a high-risk group. This study aims to explore factors influencing the acceptance of Influenza, Pertussis (T-dap), and COVID-19 vaccines among pregnant women, focusing on attitudes, social norms, perceived control, and risk perceptions. Additionally, the study compares acceptance patterns between traditional vaccines and the newer COVID-19 vaccine.
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Organ Support and Automation Technologies Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA.
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