Priorities for content for a short-course on postoperative care relevant for low- and middle-income countries: an e-Delphi process with training facilitators

Most surgical and anaesthetic mortality and morbidity occurs postoperatively, disproportionately affecting low- and middle-income countries. Various short courses have been developed to improve patient outcomes in low- and middle-income countries, but none specifically to address postoperative care and complications. We aimed to identify key features of a proposed short-course addressing this topic using a Delphi process with low- and middle-income country anaesthesia providers trained as short-course facilitators. An initial questionnaire was co-developed from literature review and exploratory workshops to include 108 potential course features. Features included content; teaching method; appropriate participants; and appropriate faculty. Over three Delphi rounds (panellists numbered 86, 64 and 35 in successive cycles), panellists indicated which features they considered most important. Responses were analysed by geographical regions: Africa, the Americas, south-east Asia and Western Pacific. Ultimately, panellists identified 60, 40 and 54 core features for the proposed course in each region, respectively. There were high levels of consensus within regions on what constituted core course content, but not between regions. All panellists preferred the small group workshop teaching method irrespective of region. All regions considered anaesthetists to be key facilitators, while all agreed that both anaesthetists and operating theatre nurses were key participants. The African and Americas regional panels recommended more multidisciplinary healthcare professionals for participant roles. Faculty from high-income countries were not considered high priority. Our study highlights variability between geographical regions as to which course features were perceived as most locally relevant, supporting regional adaptation of short-course design rather than a one-size-fits-all model.     

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.¹ Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.^2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.^4,5 Although the extent of WMHs can be visually scored,⁶ the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.⁷ Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.^8-11 More recently, a WMH segmentation challenge¹² was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.¹²Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio¹³ and have shown promising results in MS lesion detection using 3D CNNs.¹⁴ Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.¹⁵ The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.¹⁶ The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.¹⁷ Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization¹⁸ framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.     

New Zealand Emergency Medicine Network: A collaboration for acute care research in New Zealand

The specialty of emergency medicine in Australasia is coming of age. As part of this maturation there is a need for high‐quality evidence to inform practice. This article describes the development of the New Zealand Emergency Medicine Network, a collaboration of committed emergency care researchers who share the vision that New Zealand/Aotearoa will have a world‐leading, patient‐centred emergency care research network, which will improve emergency care for all, so that people coming to any ED in the country will have access to the same world‐class emergency care.     

高分辨率CT对颞部疾病的检查价值

目的：探讨高分辨率CT（HRCT）对颞部疾病的检查价值。方法：对43例颞部疾病患者行常规CT和高分辨率CT（HRCT）检查所获图像对比分析，并讨论HRCT的检查技术和图像后处理。结果：HRCT对病变的显示率及病变引起骨质破坏的程度，病变边缘，轮廓的显示均明显优于常规CT，尤其能清楚显示常规CT难以显示的中耳及内耳的细微结构，结论：高分辨率CT是颞部疾病的首选检查方法，使用高分辨率CT对颞部疾病的检查给临床提供更多，更准确的诊断信息。     

艾滋病142例临床表现分析

目的艾滋病(AIDS),是严重危害人类健康的疾病,临床上症状多变,应提高对AIDS的认识。现将作者在西非工作期间收治的142例AIDS病例的临床表现作一介绍。方法病例均为当地黑人居民,男性89例,女性53例,年龄14～45岁,平均年龄27.65岁。用酶联免疫吸附法和快速法检测患者血清中HIV—1和 HIV—2抗体。结果 HIV—1型25例(35.00％);HIV-2型74例(52.11％);混合型32例(22.53％)。临床表现为脑膜脑炎或脑炎7例(4.93％);肺型27例(19.01％);胃肠道感染84例(59.15％):包括急性48例(33.80％),慢性24例(16.90％),急腹症12例(8.45％);皮肤粘膜损害18例(12.68％):卡波齐肉瘤6例(4.23％),带状疱疹2例(1.41％),皮炎10例(7.04％),病死率19.72％(28/142)。其传播途径以性传播为主,高达78.87％(112/142),其次是密切生活接触,未发现吸毒或输血者。结论艾滋病病死率高,病情复杂,且目前尚无特效药物,必须引起医护人员高度重视。     

HPLC法测定小柴胡颗粒剂中黄芩苷的含量

目的 :建立小柴胡颗粒剂中黄芩苷的含量测定方法。方法 :采用反相高效液相色谱法。色谱柱 :Inertsil ODS- 3(5 μm,4 .6× 2 5 0 mm)柱 ;流动相 :甲醇 -水 -磷酸 (45∶ 5 5∶ 0 .2 ) ;检测波长 :315 nm。结果 :黄芩苷在 0 .2 4 2 9～ 1.2 14 4 μg/ml范围内呈现良好的线性关系 (r=0 .9999) ,回收率 10 0 .0 % ,RSD=0 .96 %。结论 :本法可用于测定小柴胡颗粒剂中黄芩苷的含量。     

PPD为丝裂源作淋巴细胞转化试验对结核性胸膜炎的诊断价值

以结核菌素纯蛋白衍生物(PPD)为丝裂源作淋巴细胞转化试验,检测结核性(17例)和非结核性(14例)胸膜炎患者的外周血淋巴细胞(PBL)和胸腔积液淋巴细胞(PEL)对PPD 的反应性。结果显示:结核性胸膜炎患者无论PPD 皮试是阳性还是阴性,其PEL 的PPD 刺激指数(PPD-SI)都显著高于PBL(P 分别<0.001与<0.01);结核性PEL 的PPD-SI 显著高于非结核性PEL(P<0.001)。提示PPD 胸腔积液淋巴细胞转化试验可作为诊断结核性胸膜炎的有效方法。