World Neurosurg
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Risk assessment is critically important in elective and high-risk interventions, particularly spine surgery. This narrative review describes the evolution of risk assessment from the earliest instruments focused on general surgical risk stratification, to more accurate and spine-specific risk calculators that quantified risk, to the current era of big data. ⋯ RATs have undergone a transformative shift from generalized risk stratification to quantitative predictive models. The next generation of tools will likely involve integration of radiographic and genetic biomarkers, machine learning, and artificial intelligence to improve the accuracy of these models and better inform patients, surgeons, and payers.
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Review
Neurosurgery and social media: A bibliometric analysis of scientific production from 2004-2023.
Neurosurgery is a rapidly advancing surgical specialty. Social media has significantly impacted the landscape of advancements in the field of neurosurgery. Research on the subject of neurosurgery and social media plays a vital role in combating disability and mortality due to neurological diseases, especially in trauma-affected individuals by increasing cooperation and sharing of clinical experiences between neurosurgeons via social media. This study aimed to evaluate the global neurosurgery and social media research performance from 2004 to 2023. ⋯ Exploring neurosurgery on social media enhances global collaboration, utilizing dynamic platforms for real-time knowledge exchange and holds immense potential for the field's global advancement.
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Spinal cord injury (SCI) is a significant public health issue, leading to physical, psychological, and social complications. Machine learning (ML) algorithms have shown potential in diagnosing and predicting the functional and neurologic outcomes of subjects with SCI. ML algorithms can predict scores for SCI classification systems and accurately predict outcomes by analyzing large amounts of data. This systematic review aimed to examine the performance of ML algorithms for diagnosing and predicting the outcomes of subjects with SCI. ⋯ ML and deep learning (DL) have shown great potential in various aspects of SCI. ML and DL algorithms have been employed multiple times in predicting and diagnosing patients with SCI. While there are studies on diagnosing acute SCI using DL algorithms, further research is required in this area.
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Lumbar degenerative disc disease (LDDD) significantly contributes to low back pain, with a complicated etiology involving genetic and environmental facts. The aim of study was to investigate the association between the TaqI (rs731236) polymorphism of the vitamin D receptor (VDR) gene with LDDD. ⋯ VDR TaqI (rs731236) GG genotype and G allele have protective potential, whereas the AA genotype and A allele are risk factors for LDDD. The findings reveal a statistically significant association of the TaqI (rs731236) polymorphism of VDR gene polymorphism with LDDD. This result highlights the potential role of genetic factors in developing LDDD and suggests avenues for future research in genetic screening and personalized treatment strategies.
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Spinal anesthesia (SA) is used in lumbar surgery, but initial adequate analgesia fails in some patients. In these cases, spinal redosing or conversion to general endotracheal anesthesia is required, both of which are detrimental to the patient experience and surgical workflow. ⋯ We found that age, larger height, and dural sac volume are risk factors for an inadequate first dose of SA. The availability of spinal magnetic resonance imaging in patients undergoing spine surgery allows the preoperative measurement of their thecal sac size. In the future, these data may be used to personalize spinal anesthesia dosing on the basis of individual anatomic variables and potentially reduce the incidence of failed spinal anesthesia in spine surgery.