World Neurosurg
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Neurosurgeons today are inundated with rapidly amassing neurosurgical research publications. Systematic reviews and meta-analyses have consequently surged in popularity because, when executed properly, they constitute a high level of evidence and may save busy neurosurgeons many hours of combing and reviewing the literature for relevant articles. Meta-analysis refers to the quantitative (and discretionary) component of systematic reviews. ⋯ Unfortunately, recent audits have found the conduct and reporting of meta-analyses in neurosurgery (but also other surgical disciplines) to be relatively lackluster in methodologic rigor and compliance to established guidelines. Some of these deficiencies can be easily remedied through better awareness and adherence to prescribed standards-which will be reviewed in this article-but others stem from inherent problems with the source data (e.g., poor reporting of original research) as well as unique constraints faced by surgery as a field (e.g., lack of equipoise for randomized trials, or existence of learning curves for novel surgical procedures, which can lead to temporal heterogeneity), which may require unconventional tools (e.g., cumulative meta-analysis) to address. Therefore, it is also our goal to take stock of the unique issues encountered by surgeons who do meta-analysis and to highlight various techniques-some of which less well-known-to address such challenges.
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Neurosurgical randomized controlled trials (RCTs) are difficult to carry out due to the low incidence of certain diseases, heterogeneous disease phenotypes, and ethical issues. This results in a weak evidence base in terms of both the number of trials and their robustness. The fragility index (FI) measures the robustness of an RCT and is the minimum number of patients in a trial whose status would have to change from a nonevent to an event to change a statistically significant result to a nonsignificant result. The smaller the FI, the more fragile the trial's outcome. ⋯ Results of neurosurgical RCTs on which we base our clinical decision-making and treatment guidelines are often fragile. Improved methodologies, international collaboration, and cooperation between specialties might improve the evidence base in the future.
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The purpose of this study was to explore the diagnostic value of convolutional neural networks (CNNs) in middle cerebral artery (MCA) stenosis by analyzing transcranial Doppler (TCD) images. ⋯ The diagnostic value of CNNs for MCA stenosis based on TCD images paralleled that of manual measurements. CNNs could be used as an auxiliary diagnostic tool to improve the diagnosis of MCA stenosis.
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It is well accepted that randomized controlled trials provide the greatest quality of evidence about effectiveness and safety of new interventions. In neurosurgery, randomized controlled trials face challenges, with their use remaining relatively low compared with other clinical areas. Adaptive designs have emerged as a method for improving the efficiency and patient benefit of trials. They allow modifications to the trial design to be made as patient outcome data are collected. The benefit they provide is highly variable, predominantly governed by the time taken to observe the primary endpoint compared with the planned recruitment rate. They also face challenges in design, conduct, and reporting. ⋯ Adaptive designs may provide benefits to neurosurgery trials and should be considered for use more widely. Use of some types of adaptive design, such as multiarm multistage, may further increase the number of interventions that can be tested with limited patient and financial resources.