基于标准化患者法评价四川农村基层医生两种慢性病诊断准确性及其影响因素

目的 运用标准化患者法评估四川农村地区基层医生不稳定型心绞痛和2型糖尿病两种慢性病诊断准确性现状,探讨基层医生两种慢性病诊断准确性的主要影响因素,为提升基层医生两种慢性病诊断准确性提供科学依据。方法 采用多阶段随机整群抽样方法,抽取四川省自贡市5个区/县50个乡镇100个村为研究现场,以调查当日在岗的全科及内科医生作为研究对象。共进行两轮数据采集,第1轮采集样本乡镇卫生院和村卫生室医生的基本信息;第1轮调查完成1个月后,运用标准化患者法开展第2轮调查,收集农村基层医生对不稳定型心绞痛和2型糖尿病诊断结果信息。运用Logistic回归分析农村基层医生不稳定型心绞痛和2型糖尿病诊断准确性的影响因素。结果 共纳入172名农村基层医生,完成186次标准化患者访问,正确诊断率为48.39%。其中不稳定型心绞痛的正确诊断率为18.68%(17/91),2型糖尿病的正确诊断率为76.84%(73/95)。Logistic回归分析显示,具有执业医师资质的农村基层医生更有可能做出正确诊断(OR=4.857,95%CI=1.076~21.933,P=0.040)。农村基层医生在诊断过程中涉及的必要问诊和检查条目越多,做出正确诊断的概率越高(OR=1.627,95%CI=1.065~2.485,P=0.024)。与不稳定型心绞痛相比,农村基层医生对2型糖尿病做出正确诊断的可能性更高(OR=6.306,95%CI=3.611~11.013,P<0.001)。结论 四川农村基层医生不稳定型心绞痛和2型糖尿病诊断准确性整体较差,建议以基层医生慢性病诊断过程质量改善为突破口,提升基层医生执业水平,进而提高慢性病诊断准确性。     

Evaluating Head Models for Cortical Source Localization of the Face-Sensitive N290 Component in Infants

Brain Topography - Accurate cortical source localization of event-related potentials (ERPs) requires using realistic head models constructed from the participant’s structural magnetic...     

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.     

A comparative study of quality of health care services of public and private hospitals in Ghana

Journal of Public Health - The standard of health care service is very important to the development of every nation as indicated in the Millennium Sustainable Development Goals. There are already a...