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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Retracted Publication
Prediction of patient's neurological recovery from cervical spinal cord injury through XGBoost learning approach.
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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The rate of elective lumbar fusion has continued to increase over the past two decades. However, there remains to be a consensus on the optimal fusion technique. This study aims to compare stand-alone anterior lumbar interbody fusion (ALIF) with posterior fusion techniques in patients with spondylolisthesis and degenerative disc disease through a systematic review and meta-analysis of the available literature. ⋯ Stand-alone-ALIF demonstrated a shorter operative time and less blood loss than the PLIF/TLIF approach. Hospitalisation time is reduced with ALIF compared with TLIF. Patient-reported outcome measures were equivocal with PLIF or TLIF. VAS and JOAS, back pain, and ODI scores mainly favoured ALIF over PLF. Adverse events were equivocal between the ALIF and posterior fusion approaches.
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This study aims to analyze the effect of pro-inflammatory cytokine-stimulated human annulus fibrosus cells (hAFCs) on the sensitization of dorsal root ganglion (DRG) cells. We further hypothesized that celecoxib (cxb) could inhibit hAFCs-induced DRG sensitization. ⋯ Cxb can inhibit PGE-2 production in hAFCs in an IL-1β-induced pro-inflammatory in vitro environment. The cxb applied to the hAFCs also reduces the sensitization of DRG nociceptors that are stimulated by the hAFCs CM.