XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury.

The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and radiographic findings. Although machine learning algorithms may increase the accuracy of outcome predictions in various fields, limited information is available on their efficacy in the management of SCI. We analyzed data from 165 patients with cervical SCI, and extracted important factors for predicting prognoses.

Ocular neuromyotonia caused by a recurrent sphenoidal ridge meningioma.

Background: Ocular neuromyotonia (ONM) is a rare ocular motility disorder characterized by involuntary paroxysmal extraocular muscle contraction and is caused by radiation therapy, vascular compression, and inflammatory disease. This study includes a rare case of ONM caused by a recurrent meningioma.

Case Description: A 56-year-old man presented with diplopia due to the right oculomotor nerve palsy caused by a sphenoidal atypical meningioma, with improved symptoms after initial surgery.