World Neurosurg
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Review Meta Analysis
Reliability of Preoperative Prediction of the Location of the Facial Nerve Using Diffusion Tensor Imaging-Fiber Tracking in Vestibular Schwannoma: A Systematic Review and Meta-Analysis.
The popularization and application of microscopy, the in-depth study of the microanatomy of the cerebellopontine angle, and the application of intraoperative electrophysiological monitoring technology to preserve facial nerve function have laid a solid foundation for the modern era of neurosurgery. The preoperative prediction of the location of the facial nerve is a long-desired goal of neurosurgeons. The advances in neuroimaging seem to be making this goal a reality. ⋯ The estimated overall intraoperative verification concordance rate was 89.05% (95% confidence interval 85.06%-92.58%). Preoperatively predicting the location of the facial nerve using diffusion tensor imaging-fiber tracking in vestibular schwannoma is reliable, but the extent to which it contributes to long-term facial nerve function is still unclear. To further verify these results, studies with larger sample sizes are needed in the future, especially prospective randomized controlled trials focusing on the long-term functional preservation of the facial nerve.
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Review Meta Analysis
Current Accuracy of Augmented Reality Neuronavigation Systems: Systematic Review and Meta-Analysis.
Augmented reality neuronavigation (ARN) systems can overlay three-dimensional anatomy and disease without the need for a two-dimensional external monitor. Accuracy is crucial for their clinical applicability. We performed a systematic review regarding the reported accuracy of ARN systems and compared them with the accuracy of conventional infrared neuronavigation (CIN). ⋯ In ARN, there seems to be lack of agreement regarding the best method to assess accuracy. Nevertheless, ARN systems seem able to achieve an accuracy comparable to CIN systems. Future studies should be prospective and compare TREs, which should be measured in a standardized fashion.
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Review Meta Analysis
Is lumbar fusion necessary for chronic low back pain associated with degenerative disc disease? A meta-analysis.
We sought to evaluate the efficacy and safety of lumbar fusion versus nonoperative care for the treatment of chronic low back pain associated with degenerative disk disease. ⋯ The present meta-analysis determined that fusion surgery was no better than nonoperative treatment in terms of the pain and disability outcomes at either short- or long-term follow-up. It is necessary for clinicians to weigh the risk of complications associated with fusion surgery against additional surgeries after nonoperative treatment. Considering lax patient inclusion criteria in the existing randomized clinical trials, the result needs to be further confirmed by high-quality research with stricter selection criteria in the future.
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We searched PubMed, Embase, and the Cochrane Library for randomized controlled trials from January 1980 to April 2018 for adolescents with mild traumatic brain injury (mTBI) to explore the value of aerobic exercise in sport-related concussion (SRC) and mTBI treatment. ⋯ Compared with usual treatment, aerobic exercise promoted mTBI adolescents' recovery, assessed by PCSS and time to recovery. However, aerobic exercise may not help with neurocognitive function recovery.
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Machine learning has emerged as a viable asset in the setting of pituitary surgery. In the past decade, the number of machine learning models developed to aid in the diagnosis of pituitary lesions and predict intraoperative and postoperative complications following transsphenoidal surgery has increased exponentially. As computational processing power continues to increase, big data sets continue to expand, and learning algorithms continue to surpass gold standard predictive tools, machine learning will serve to become an important component in improving patient care and outcomes. ⋯ The field of machine learning is broad, with radiomics and artificial neural networks comprising 2 commonly used supervised learning methods in pituitary surgery. Given the large heterogeneity of pituitary and sellar lesions, the promise of machine learning lies in its ability to identify relationships and patterns that are otherwise hidden from standard statistical methods. While machine learning has great potential as a clinical adjunct during the surgical preplanning process and in predicting complications and outcomes, challenges moving forward include standardization and validation of these paradigms.