Neurosurgery
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"Moral distress" describes the psychological strain a provider faces when unable to uphold professional values because of external constraints. Recurrent or intense moral distress risks moral injury, burnout, and physician attrition but has not been systematically studied among neurosurgeons. ⋯ We developed a reliable survey assessing neurosurgical moral distress. Nearly, half of neurosurgeons suffered moral distress within the past year, most intensely from external pressure to perform futile surgery. Moral distress correlated with burnout risk caused 10% of neurosurgeons to leave a position and a quarter to consider leaving.
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Bone density has been associated with a successful fusion rate in spine surgery. Hounsfield units (HUs) have more recently been evaluated as an indirect representation of bone density. Low preoperative HUs may be an early indicator of global disease and chronic process and, therefore, indicative of the need for future reoperation. ⋯ Patients who underwent lumbar interbody fusion that did not require reoperation for adjacent-level degeneration were found to have a higher mean preoperative HU than patients who did require surgical intervention. Lower preoperative CT HU was a significant independent predictor for the requirement of adjacent-level surgery after spinal arthrodesis.
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Dural arteriovenous fistulas (dAVFs) are often treated with stereotactic radiosurgery (SRS) to achieve complete obliteration (CO), prevent future hemorrhages, and ameliorate neurological symptoms. ⋯ SRS for dAVFs results in CO in the majority of patients with excellent symptom improvement rates with minimal toxicity. Patients with NCS and/or higher-grade dAVFs have poorer symptom cure rates. Combined therapy with embolization and SRS is recommended when feasible for clinically aggressive dAVFs or those refractory to embolization to maximize the likelihood of symptom cure.
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Survival prediction of patients affected by brain tumors provides essential information to guide surgical planning, adjuvant treatment selection, and patient counseling. Current reliance on clinical factors, such as Karnofsky Performance Status Scale, and simplistic radiological characteristics are, however, inadequate for survival prediction in tumors such as glioma that demonstrate molecular and clinical heterogeneity with variable survival outcomes. ⋯ Here, we provide an overview of current literature that apply computational analysis tools such as radiomics and machine learning methods to the pipeline of image preprocessing, tumor segmentation, feature extraction, and construction of classifiers to establish survival prediction models based on neuroimaging. We also discuss challenges relating to the development and evaluation of such models and explore ethical issues surrounding the future use of machine learning predictions.