Neurosurgery
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Machine learning (ML) holds promise as a tool to guide clinical decision making by predicting in-hospital mortality for patients with traumatic brain injury (TBI). Previous models such as the international mission for prognosis and clinical trials in TBI (IMPACT) and the corticosteroid randomization after significant head injury (CRASH) prognosis calculators can potentially be improved with expanded clinical features and newer ML approaches. ⋯ We developed high-performing well-calibrated ML models for predicting in-hospital mortality for both the HIC and LMIC settings that have the potential to influence clinical management and traumatic brain injury patient trajectories.
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Despite spinal cord stimulation's (SCS) proven efficacy, failure rates are high with no clear understanding of which patients benefit long term. Currently, patient selection for SCS is based on the subjective experience of the implanting physician. ⋯ This combined unsupervised-supervised learning approach yielded high predictive performance, suggesting that advanced ML-derived approaches have potential to be used as a functional clinical tool to improve long-term SCS outcomes. Further studies are needed for optimization and external validation of these models.
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Data regarding the safety and effectiveness of stent placement in small vessels (<2 mm in diameter) for treating wide-necked cerebral aneurysms are limited. ⋯ Stent-assisted coil embolization for unruptured cerebral aneurysms using stents, especially the Neuroform Atlas, in small arteries <2 mm in diameter is effective and feasible, but careful perioperative attention should be given to thrombotic events during the embolization of middle cerebral artery aneurysms.