Journal of neurosurgery
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Journal of neurosurgery · Oct 2024
External assessment of preoperative scores for predicting outcome after microvascular decompression for trigeminal neuralgia.
Recently, two scoring systems have been developed for predicting pain-free outcomes after microvascular decompression (MVD). Evaluation of these scores on large external datasets has been limited. In this study, the authors aimed to evaluate the performance of published MVD scoring systems in predicting pain-free outcome. ⋯ Both the Hardaway and Panczykowski scores may be useful for predicting postoperative pain-free duration in TN patients, and their utility may be greatest when scores are clustered. Continued refinement of both scoring systems will help to improve our ability to predict patient outcomes after MVD.
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Journal of neurosurgery · Oct 2024
Spin in traumatic brain injury literature: prevalence and associated factors. A systematic review.
Spin is characterized as a misinterpretation of results that, whether deliberate or unintentional, culminates in misleading conclusions and steers readers toward an excessively optimistic perspective of the data. The primary objective of this systematic review was to estimate the prevalence and nature of spin within the traumatic brain injury (TBI) literature. Additionally, the identification of associated factors is intended to provide guidance for future research practices. ⋯ The prevalence of spin in the TBI literature is high, even at leading medical journals. Studies with higher risks of bias are more frequently associated with spin. Critical interpretation of results and authors' conclusions is advisable regardless of the study design and published journal.
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Journal of neurosurgery · Oct 2024
Evaluation of the Glasgow Coma Scale-Pupils score for predicting inpatient mortality among patients with traumatic subdural hematoma at United States trauma centers.
The Glasgow Coma Scale-Pupils (GCS-P) score has been suggested to better predict patient outcomes compared with GCS alone, while avoiding the need for more complex clinical models. This study aimed to compare the prognostic ability of GCS-P versus GCS in a national cohort of traumatic subdural hematoma (SDH) patients. ⋯ The GCS-P score provides better short-term prognostication compared with the GCS score alone among traumatic SDH patients with penetrating TBI. The GCS-P score overestimates inpatient mortality risk among penetrating TBI patients with higher rates of observed mortality. For penetrating TBI patients, which comprised 2.4% of our SDH cohort, a low GCS-P score should not justify clinical nihilism or forgoing aggressive treatment.
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Journal of neurosurgery · Oct 2024
Optimal hippocampal targeting in responsive neurostimulation for mesial temporal lobe epilepsy.
The aim of this study was to identify features of responsive neurostimulation (RNS) lead configuration and contact placement associated with greater seizure reduction in mesial temporal lobe epilepsy (MTLE). ⋯ Dual unilateral hippocampal implantation increased RNS contact density in patients with unilateral MTLE, which contributed to improved outcomes, not by stimulating more of the hippocampus, but instead by being more likely to stimulate a latent subtarget in the anterior hippocampus. It remains to be explored whether a single electrode targeted selectively to this region would also result in improved outcomes.
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Journal of neurosurgery · Oct 2024
Development and validation of machine learning models to predict postoperative infarction in moyamoya disease.
Cerebral infarction is a common complication in patients undergoing revascularization surgery for moyamoya disease (MMD). Although previous statistical evaluations have identified several risk factors for postoperative brain ischemia, the ability to predict its occurrence based on these limited predictors remains inadequately explored. This study aimed to assess the feasibility of machine learning algorithms for predicting cerebral infarction after revascularization surgery in patients with MMD. ⋯ This study indicates the usefulness of employing machine learning techniques with routine perioperative data to predict the occurrence of cerebral infarction after revascularization procedures in patients with MMD.