后外侧结构重建在新鲜股骨颈骨折人工股骨头置换术中的应用

目的探讨后外侧结构重建对后外侧入路人工股骨头置换术术后早期关节脱位的影响。方法选取2016年9月至2017年8月于我院行后外侧入路初次人工股骨头置换术的股骨颈骨折患者60例,根据术中是否修补关节囊及外旋肌群分为重建组(33例:舌形切开关节囊,术中将关节囊及外旋肌群原位缝合在大转子后方及臀中肌肌腱附着处)和对照组(27例:切除关节囊后,术中未进行外旋肌群修复重建)。比较两组的手术情况及术后近期关节功能情况。结果重建组的手术时间为(45.0±15.3) min,长于对照组的(35.0±12.4) min (P <0.05)。重建组术腔引流量为(200.0±80.0) m L,少于对照组的(420.0±120.6) m L (P <0.05)。重建组的早期脱位率为0.000%(0例),与对照组的7.407%(2例)比较无统计学差异(P>0.05)。重建组术后Harris评分为(92.0±3.4)分,高于对照组的(88.2±5.0)分(P <0.05)。结论在后外侧入路人工股骨头置换过程中行后外侧结构重建能够有效减少术腔引流量,提高髋关节Harris评分,对维持髋关节软组织平衡具有重要意义。     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.