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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Due to the diversity of patient characteristics, therapeutic approaches, and radiological findings, it can be challenging to predict outcomes based on neurological consequences accurately within cervical spinal cord injury (SCI) entities and based on machine learning (ML) technique. Accurate neurological outcomes prediction in the patients suffering with cervical spinal cord injury is challenging due to heterogeneity existing in patient characteristics and treatment strategies. Machine learning algorithms are proven technology for achieving greater prediction outcomes. ⋯ Thus, with the proposed XGBoost approach, the enhanced accuracy in reaching the outcome is 81.1%, and from other models such as decision tree (80%) and logistic regression (82%), in predicting outcomes of neurological improvements within cervical SCI patients. Considering the AUC, the XGBoost and decision tree valued with 0.867 and 0.787, whereas logistic regression showed 0.877. Therefore, the application of XGBoost for accurate prediction and decision-making in the categorization of pre-treatment in patients with cervical SCI has reached better development with this study.
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Observational Study
Association between anemia and lumbar disc degeneration in patients with low back pain: an observational retrospective study.
The goal of this study was to evaluate the possible associations between anemia and morphologic features of lumbar disc degeneration (LDD) in subjects with low back pain. ⋯ The results suggested that patients with anemia, and those without are radiologically different with varying patterns of DD. Patients with back pain and anemia were more likely to have extensive and severe DD. Although relevant mechanisms are not yet well understood, this study improved our understandings of the pathophysiology for LDD.
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This study examines the test-retest reliability, the minimal clinically important difference (MCID), and its correlation with the Rolland Morris Disability Questionnaire (RMDQ) of the Dubousset Functional Test (DFT) in evaluating the functional capacity and dynamic balance of patients with chronic non-specific low back pain (cnsLBP). ⋯ The DFT is reliable in evaluating patients' functional capacity and dynamic balance with cnsLBP without causing discomfort. It is simple, quick, and simultaneously assesses multiple areas contributing to spinal alignment, muscle integrity, and balance.
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Treatment selection for idiopathic scoliosis is informed by the risk of curve progression. Previous models predicting curve progression lacked validation, did not include the full growth/severity spectrum or included treated patients. The objective was to develop and validate models to predict future curve angles using clinical data collected only at, or both at and prior to, an initial specialist consultation in idiopathic scoliosis. ⋯ The models can help clinicians predict how much curves would progress without treatment at future timepoints of their choice using simple variables. Predictions can inform treatment prescription or show families why no treatment is recommended. The nonlinear effects of time account for the rapid increase in curve angle at the beginning of growth and the slowed progression after maturity. These validated models predicted future Cobb angle with good accuracy in untreated idiopathic scoliosis over the full growth spectrum.
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The primary aim was to measure the safe corridor (SC), surgical incision anterior margin (AM), and posterior margin (PM) for OLIF bilaterally from L1 to L5. The secondary aim was to determine the feasibility of approach via the SC. The tertiary aim was to analyze the influence of demographic and anthropometric factors on OLIF parameters. ⋯ This study is the first to provide guidelines on the appropriate location of the incision line during OLIF based on SC from L1 to L5. SC measurements do not vary by sex. OLIF is more feasible via a left-sided approach.