Assessing perceived quality of primary care under hospital-township health centre integration: A cross-sectional study in China

Since the new round of health care reform in 2009, the vertical integration of hospitals and primary health institutions has become widely implemented in China as an efficient method for improving quality of primary care. This study aimed to answer the following questions: (a) What is the perceived quality of township health centres (THCs) under integration? (B) What differences could be observed among the three typical integration models, namely, private hospital-THC integration, public hospital-THC integration, and loose collaboration? Two rounds of cross-sectional surveys were conducted from November 2016 to June 2018. The Chinese version of the Primary Care Assessment Tool was used to evaluate perceived quality of sample THCs, and 1118 adult patients were interviewed in total. Multiple linear regressions were employed to compare the quality scores between two survey rounds and among different integration models after controlling for potential confounders. The results revealed that the quality of care significantly improved under private hospital-THC integration as observed by comparing two survey rounds, while no change or slight changes were observed in the other two models. The difference observed among the three models was that the perceived quality of THCs integrated with private hospitals was worse than that of THCs integrated with public hospitals and THCs under loose collaboration, while no significant difference was observed between public hospital-THC integration and loose collaboration. Increased attention should be given to highlighting the tight integration between hospitals and THCs and the different roles played by private and public hospitals in the current reform.     

Uncertainty evaluation at three spatial scales for the NDVI-based VWC estimation method used in the SMAP algorithm

Vegetation water content (VWC) is the key input parameter for a soil moisture retrieval algorithm based on microwave remote sensing, and VWC uncertainty can limit the estimated accuracy of soil moisture. There has been little research on VWC algorithm development and validation in China, and the uncertainty of the VWC estimation method has not been well evaluated. Therefore, the aim of this study is to evaluate the uncertainty of the VWC estimation method used in the SMAP (Soil Moisture Active Passive) algorithm on three spatial scales (the point-scale, 30 m scale, and 1 km scale) for maize in northeast China. Results from three ground experimental datasets showed that the SMAP VWC estimation method was strongly biased with an average overestimation of 1.16 kg m^?2,1.04 kg m^?2, and 1.13 kg m^?2 for the point-scale, 30 m scale, and 1 km scale respectively, and maximum bias occurred in the mid-stage of maize. Also, a new power relationship between NDVI (Normalized Difference Vegetation Index) and VWC was proposed for the 30 m scale based on Sentinel 2 NDVI and field VWC values from 2017 experiment, with respective R² (coefficient of determination) and Root Mean Squared Error (RMSE) values of 0.80 and 0.67 kg m^?2. The results confirmed that this power relationship was still suitable for VWC estimation at the 1 km scale, and it has smaller bias than the original SMAP VWC method. Future work will be carried out to evaluate the applicability of this VWC estimation method over a lager region. It is expected that it can improve the accuracy of soil moisture by providing high precision VWC input parameters.     

中日友好医院分型股骨头坏死腓骨植入治疗的三维有限元分析

文题释义：股骨头坏死中日友好医院分型的有限元分析：根据李子荣等提出的中日友好医院分型，建立股骨头坏死三维模型，分为 M型(内侧型)、C型(中央型)和 L型(外侧型)，其中 L型包括L1型(次外侧型)、L2型(极外侧型)和 L3型(全头型)。通过对建立的模型进行有限元分析，为该分型的保髋治疗提供了一定力学依据，显示外侧柱的存留是精准预防塌陷的重要因素，为进一步实现个体化治疗提供力学基础。 腓骨支撑坏死股骨头保髋手术：是对于早中期股骨头坏死需要保留股骨头患者进行的一种手术方式。首先需对股骨头进行髓芯减压，清除一定坏死骨，空腔填塞松质骨(髂骨为主)，打压结实后植入腓骨(异体或自体)支撑，给坏死区的提供力学支撑及生物学修复，预防股骨头进一步坏死及塌陷。 背景：研究报道股骨头坏死的保髋疗效与外侧柱存留密切相关，中日友好医院分型是根据三柱结构确立的，对股骨头塌陷的预测准确性高。 目的：建立股骨头坏死中日友好医院分型各分型仿真的三维有限元模型，通过有限元分析各分型腓骨植入的力学变化，探讨外侧柱存留对保髋疗效的意义，为该分型的塌陷精准预测提供基础。 方法：建立正常股骨头、中日友好医院分型(M型、C型、L1型、L2型、L3型)股骨头坏死及其腓骨植入3组11种三维有限元模型，运用ANSYS软件进行有限元分析计算，观察各组模型的最大应力值、最大位移值及股骨头内部载荷传递模式。 结果与结论：①坏死组位移最大，应变最大，且因坏死分型不同而位移不同，位移变化如下：M型相似文献   

Network Meta-Analysis of Randomized Controlled Trials: Efficacy and Safety of UDCA-Based Therapies in Primary Biliary Cirrhosis

Major ursodeoxycholic acid (UDCA)-based therapies for primary biliary cirrhosis (PBC) include UDCA only, or combined with either methotrexate (MTX), corticosteroids (COT), colchicine (COC), or bezafibrate (BEF). As the optimum treatment regimen is unclear and warrants exploration, we aimed to compare these therapies in terms of patient mortality or liver transplantation (MOLT) and adverse events (AE).PubMed, the Cochrane Library, and Scopus were searched for randomized controlled trials up to August 31, 2014. We estimated the hazard ratios (HRs) for MOLT and odds ratios (ORs) for AE. A sensitivity analysis based on the dose of UDCA was also executed.Thirty-one eligible articles were included. Compared with COT plus UDCA, UDCA (HR 0.38, 95% confidence interval [CI] 0.09–1.39), BEF plus UDCA (HR 0.29, 95% CI 0.02–4.83), COC plus UDCA (HR 0.39, 95% CI 0.07–2.25), MTX plus UDCA (HR 0.28, 95% CI 0.05–1.63), or OBS (HR 0.49, 95% CI 0.11–2.01) all provided an increased risk of MOLT. With respect to drug AE profile, although not differing appreciably, BEF plus UDCA was associated with more AEs compared with UDCA (OR 3.16, 95% CI 0.59–20.67), COT plus UDCA (OR 2.27, 95% CI 0.15–33.36), COC plus UDCA (OR 1.00, 95% CI 0.09–12.16), MTX plus UDCA (OR 2.03, 95% CI 0.23–17.82), or OBS (OR 3.00, 95% CI 0.53–20.75). The results of sensitivity analyses were highly consistent with previous analyses.COT plus UDCA was the optimal UDCA-based regimen for both MOLT and AEs. BEF plus UDCA was most likely to cause AEs, whereas monotherapy with UDCA and coadministriation of COT plus UDCA appeared to be associated with the fewest AEs for PBC treatment.     

基于剂量组学预测肺癌患者放射性肺炎发生的研究

目的 探讨剂量组学在预测肺癌根治性放疗患者放射性肺炎发生中的应用潜能。方法 回顾性收集行根治性放疗的314例肺癌患者的临床资料、放疗剂量文件、定位及随访CT图像,根据临床资料及影像学随访资料对放射性肺炎进行分级,提取全肺的剂量组学特征,构建机器学习模型。应用1000次自助抽样法（bootstrap）的最小绝对值收敛和选择算子嵌套逻辑回归（LASSO‐LR）及1000次bootstrap的赤池信息量准则（AIC）向后法筛选与放射性肺炎相关的剂量组学特征,随机按照7∶3划分为训练集及验证集,应用逻辑回归建立预测模型,并应用ROC曲线及校正曲线评价模型的性能。结果 共提取120个剂量组学特征,经LASSO‐LR降维筛选得到12个特征进入“特征池”,再经过AIC向后法筛选,最终筛选出6个剂量组学特征进行模型构建,训练集AUC为0.77（95%CI为0.65~0.87）,独立验证集AUC为0.72（95%CI为0.64~0.81）。结论 利用剂量组学建立的预测模型具有预测放射性肺炎发生的潜力,但仍需继续纳入多中心数据及前瞻性数据进一步挖掘剂量组学的应用潜能。