World Neurosurg
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Randomized Controlled Trial
Machine Learning-Based Prognostic Model for the Prediction of Early Death after Traumatic Brain Injury: Comparison with the Corticosteroid Randomization after Significant Head Injury (CRASH) Model.
Machine learning (ML) has been used to predict the outcomes of traumatic brain injury. However, few studies have reported the use of ML models to predict early death. This study aimed to develop ML models for early death prediction and to compare performance with the corticosteroid randomization after significant head injury (CRASH) model. ⋯ The ML models may have comparable performances compared to the CRASH model despite being developed with a smaller sample size.
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Review Meta Analysis
Outcomes Following Penetrating Brain Injuries in Military Settings: A Systematic Review and Meta-Analysis.
While neurosurgeons are experienced in treating penetrating brain injuries (PBIs) in civilian settings, much less is known about management and outcomes of PBIs in military settings. ⋯ In this first systematic review and meta-analysis of outcomes following combat-related PBIs, a GCS score >8 at presentation was found to be an important predictor of a favorable GOS and decreased mortality.
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Research on the effects of substance use disorders (SUDs) on postoperative outcomes within neurosurgical oncology has been limited. Therefore, the present study sought to quantify the effect of having a SUD on hospital length of stay, postoperative complication incidence, discharge disposition, hospital charges, 90-day readmission rates, and 90-day mortality rates following brain tumor surgery. ⋯ In patients with brain tumor, SUDs significantly and independently predict 90-day hospital readmission after surgery. Targeted management of patients with SUDs before and after surgery can optimize patient outcomes and improve the provision of high-value neurosurgical care.