Spine
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Critical spinal epidural pathologies can cause paralysis or death if untreated. Although magnetic resonance imaging is the preferred modality for visualizing these pathologies, computed tomography (CT) occurs far more commonly than magnetic resonance imaging in the clinical setting. ⋯ A machine learning model for identifying spinal epidural hematomas and abscesses on CT can be implemented in a clinical workflow.
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A post hoc analysis. ⋯ ASD prediction models for MC, RA, and RO performed better than chance in a cohort of adult lumbar scoliosis patients, though the homogeneity of ASLS affected calibration and accuracy. Optimization of models require samples with the breadth of outcomes (0%-100%), supporting the need for continued data collection as personalized prediction models may improve decision-making for the patient and surgeon alike.
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Multicenter Study
Establishment of an Individualized Distal Junctional Kyphosis Risk Index following the Surgical Treatment of Adult Cervical Deformities.
A retrospective review of a multicenter comprehensive cervical deformity (CD) database. ⋯ This study proposes a novel risk index of DJK development that focuses on potentially modifiable surgical factors as well as established patient-related and radiographic determinants. The reference model created demonstrated strong correlations with relevant two-year outcome measures, including axial pain-related symptoms, occurrence of related reoperations, and the achievement of minimal clinically importance differences for 5-dimension EuroQol questionnaire.
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Multicenter Study
Deriving a Novel Score Predicting Progression in Early-Onset Scoliosis: A Multicenter Initiative.
This was a retrospective multicenter study. ⋯ Level 3.
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A retrospective study at a single academic institution. ⋯ The predictive models developed in this study can enable accurate preoperative estimation of LOS and risk of rehabilitation discharge for adult patients undergoing elective spine surgery. The demonstrated models exhibited better performance than NSQIP for prediction of LOS and equivalent performance to NSQIP for prediction of discharge location.