Neurosurgery
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Reports suggest that phosphatidylinositol 3-kinase pathway alterations confer increased risk of progression and poor prognosis in oligodendroglioma, IDH-mutant, and 1p/19q-codeleted molecular oligodendrogliomas (mODG). However, factors that affect prognosis in mODG have not been thoroughly studied. In addition, the benefits of adjuvant radiation and temozolomide (TMZ) in mODGs remain to be determined. ⋯ Our findings suggest that mODGs harboring PIK3CA mutations have worse OS. Except for an advantage in PFS with TMZ treatment, adjuvant TMZ, radiation, or a combination of the two showed no significant improvement in OS.
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Spine surgery outcomes assessment currently relies on patient-reported outcome measures, which satisfy established reliability and validity criteria, but are limited by the inherently subjective and discrete nature of data collection. Physical activity measured from smartphones offers a new data source to assess postoperative functional outcomes in a more objective and continuous manner. ⋯ The perioperative clinical course of patients undergoing spine surgery was systematically classified using smartphone-based mobility data. Our findings highlight the potential utility of such data in a novel quantitative and longitudinal surgical outcome measure.
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Recovery time after corpus callosotomy (CC) is known to be longer in elderly than in younger patients. ⋯ Early ADL recovery after 1-stage complete CC is favorable in both young and adult patients. These findings, with good surgical outcomes, will encourage more positive consideration of 1-stage complete CC in both pediatric and adult patients.
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The authors have developed pretrained machine learning (ML) models to evaluate neonatal head shape deformities using top-down and facial orthogonal photographs of the patient's head. In previous preliminary analysis, this approach was tested with images from an open-source data bank. ⋯ Machine learning-driven image analysis represents a promising strategy for the identification of craniosynostosis in a real-world practice setting. This approach has potential to reduce the need for imaging and facilitate referral by primary care providers.
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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.