European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
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Multicenter Study
Central sensitization as a predictive factor for the surgical outcome in patients with lumbar spinal stenosis: a multicenter prospective study.
The impact of central sensitization (CS) on neurological symptoms and surgical outcomes in patients with lumbar spinal stenosis (LSS) remains unknown. This study aimed to investigate the influence of preoperative CS on the surgical outcomes of patients with LSS. ⋯ Preoperative CS evaluated by CSI had a significantly worse impact on surgical outcomes, including neurological symptoms, disability, and QOL, especially related to LBP and psychological factors. CSI can be used clinically as a patient-reported measure for predicting postoperative outcomes in patients with LSS.
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Multicenter Study
The Norwegian degenerative spondylolisthesis and spinal stenosis (NORDSTEN) study: study overview, organization structure and study population.
To provide an overview of the The Norwegian Degenerative spondylolisthesis and spinal stenosis (NORDSTEN)-study and the organizational structure, and to evaluate the study population. ⋯ The NORDSTEN study provides opportunity to investigate clinical course of LSS with or without surgical interventions. The NORDSTEN-study population were similar to LSS patients treated in routine surgical practice, supporting the external validity of previously published results.
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Multicenter Study
Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images.
To develop and validate a deep learning (DL) model for detecting lumbar degenerative disease in both sagittal and axial views of T2-weighted MRI and evaluate its generalized performance in detecting cervical degenerative disease. ⋯ The proposed DL model can automatically detect lumbar and cervical degenerative disease on T2-weighted MR images with good performance, robustness, and feasibility in clinical practice.
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Multicenter Study
Deep learning-based prediction model for postoperative complications of cervical posterior longitudinal ligament ossification.
Postoperative complication prediction helps surgeons to inform and manage patient expectations. Deep learning, a model that finds patterns in large samples of data, outperform traditional statistical methods in making predictions. This study aimed to create a deep learning-based model (DLM) to predict postoperative complications in patients with cervical ossification of the posterior longitudinal ligament (OPLL). ⋯ A new algorithm using deep learning was able to predict complications after cervical OPLL surgery. This model was well calibrated, with prediction accuracy comparable to that of regression models. The accuracy remained high even for predicting only neurological complications, for which the case number is limited compared to conventional statistical methods.
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Multicenter Study
Trends in cervical laminoplasty and 30-day postoperative complications: 10-year results from a retrospective, multi-institutional study of 1095 patients.
This study aimed to investigate the recent 10-year trends in cervical laminoplasty and 30-day postoperative complications. ⋯ From 2008 to 2017, there were trends toward increasing age at surgery and surgeons' preference for refined open-door laminoplasty. The 30-day complication rate remained stable, but the C5 palsy rate halved.