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| 1 | 指南2.0:为成功制定指南而系统研发的全面清单显示文摘背景虽然当前已有一些评估卫生保健指南可靠性的工具,但仍缺乏针对制定指南实际步骤的指导。针对指南制定者们所需考虑的相关资源和工具,我们系统研发了一份全面的条目清单,但这并不意味着每篇指南都需遵守该清单的所有条目。方法我们检索了国际指南制定机构的指南制定手册、指南的指南(主要是来自国际和国家机构以及专业学会的方法学报告),以及提供系统指导的最新文章。经过反复评价这些资料,尽可能全面地罗列和提取条目,并制定与指南有关的重要主题。通过反复讨论,我们对条目进行评价以去重和补漏,同时邀请指南制定专家对所增加的条目进行修改并提出建议。结果我们制定了一份包含18个主题、146个条目的清单,并建立了帮助指南制定者应用这些条目的网站。这些主题和条目涵盖了指南从规划、完成、实施和评估的全过程。最终的清单版本也包括了培训所需的资料以及应用这些条目时用到的方法学参考文献的链接。解释本清单将提供给指南制定者用作参考。仔细考虑清单中的条目将有助于指南的制定、实施和评估,我们也将会通过大众反馈来修订并持续更新清单。 | Holger J. Schunemann Wojtek Wiercioch Itziar Etxeandia Maicon Falavigna Nancy Santesso Reem Mustafa Matthew Ventresca Romina Brignardello-petersen] Kaja-Triin Laisaar Sergio Kowalski Tejan Baldeh Yuan Zhang Uiia Raid Ignacio Neumann Susan L. Norris Judith Thornton Robin Harbour Shaun Treweek Gordon Guyatt Pablo Alonso-Coello Marge Reinap Jan Brozek Andrew Oxman Elie A. Akl | 2014 | 中国循证医学杂志2014，14，9： | 26 |
| 2 | 钠-葡萄糖共转运蛋白-2抑制剂或胰高血糖素样肽-1受体激动剂治疗成人2型糖尿病:临床实践指南显示文摘临床问题对于存在不同心血管风险及肾脏结局的2型糖尿病患者,在原有生活方式干预和/或其他降糖药物的基础上加用钠-葡萄糖共转运蛋白2(SGLT-2)抑制剂和胰高血糖素样肽1(GLP-1)受体激动剂的获益及风险是什么?现行做法几十年来,2型糖尿病的治疗决策都以控制血糖为主导。SGLT-2抑制剂和GLP-1受体激动剂在传统观念中常被用于二甲双胍治疗后血糖仍控制不佳的患者。目前这一现状已经发生了改变,这得益于多项临床研究结果。研究显示SGLT-2抑制剂和GLP-1受体激动剂拥有独立于药物降糖作用之外的对于动脉粥样硬化性心血管病(CVD)和慢性肾脏病(CKD)的获益。建议本指南阐述了针对不同风险分层的成人2型糖尿病患者使用SGLT-2抑制剂或GLP-1受体激动剂的建议。•伴有3种或更少的心血管风险因素且不存在CVD或CKD:不建议启动SGLT-2抑制剂或GLP-1受体激动剂治疗。(推荐等级:弱)•伴有3种以上心血管风险因素且不存在CVD或CKD:建议启动SGLT-2抑制剂治疗,不建议启动GLP-1受体激动剂治疗。(推荐等级:弱)•已经存在CVD或CKD:建议启动SGLT-2抑制剂治疗和GLP-1受体激动剂治疗。(推荐等级:弱)•已经存在CVD和CKD:建议启动SGLT-2抑制剂治疗(推荐等级:强)和GLP-1受体激动剂治疗。(推荐等级:弱)•对于那些想要进一步降低CVD和CKD结局风险的患者:推荐优先启用SGLT-2抑制剂治疗而非GLP-1受体激动剂治疗。(推荐等级:弱)这项指南是如何制订的一个由患者、临床医生和方法学家共同组成的国际小组提出了这些推荐意见。这些推荐意见基于可信度较高的指南的标准,并使用GRADE分级方法进行评估。该小组采用了息者个体化的观点。证据一项关于获益与风险的系统综述和网络meta分析(764项随机对照研究,包括421346例参与者)发现SGLT-2抑制剂和GLP-1受体激动剂可以降低总体死亡率、心肌梗死发生率、终末期肾病或肾衰竭的发生率(中等至高等质量的证据)。在不同的亚组中这些药物对卒中、因心力衰竭所致住院和其他主要不良事件有不同的影响。药物绝对获益的程度因患者个体风险的不同有很大的差异。(例如,对于接受了超过5年药物治疗的1000例患者,在最低风险人群中死亡人数减少了5人,在最高风险人群中死亡人数减少了48人)。一项关于预后的综述确认了14种风险预测模型,其中一种(RECODe)在证据总结中报告了大部分基线风险评估数据,小组利用该模型以支持风险分层的建议。考虑到患者的价值观及个体差异,指南推荐的支撑证据包括一项对已发表论文的系统综述、一项患者焦点小组研究、一项临床问题总结,以及一项指南调查。指南解读我们依据不同的CVD和CKD风险水平,综合考虑获益、风险和其他因素的平衡,以及每一个风险组别的实际问题,来对推荐意见进行分层。本指南强烈建议CVD和CKD患者使用SGLT-2抑制剂治疗,这说明专家组认为其具有显著的获益。而对于其他成人2型糖尿病患者,推荐等级较弱,这说明专家组想要在获益、风险及治疗花费上取得一个更好的平衡。临床医生通过该指南可以使用可靠的风险计算模型,如RECODe,来明确其患者的个体心血管和肾脏疾病风险。医患交互式总结临床证据和制订决策有助于患者知晓治疗选择,包括进行共同决策。2型糖尿病人群(全球患病率不断增长1-2)正面临着不断增加的心血管疾病、肾脏病和其他并发症的风险3。数十年来,2型糖尿病的管理始终以控制血糖及糖化血红蛋白(HbA1c)为治疗目标4-5,但是,最近的高质量随机对照研究已经对这种以血糖为中心的治疗模式发起了挑战。研究结果显示,强化血糖控制未必会降低大血管不良事件,它还可能带来不利影响监管机构现在要求新型糖尿病药物必须证明其具有心血管和肾脏获益才能获得批准。对两类新药--钠-葡萄糖共转运蛋白2(SGLT-2)抑制剂和胰高血糖素样肽1(GLP-1)受体激动剂(见框图1)的临床试验结果显示,在现有治疗方案(常规治疗)之上加用这些药物,对死亡、心肌梗死、卒中、心力衰竭和肾脏的结局(如进展为终末期肾病)都有获益8-12。 | Sheyu Li Per Olav Vandvik Lyubov Lytvyn Gordon H Guyatt Suetonia C Palmer Rene Rodriguez-Gutierrez Farid Foroutan Thomas Agoritsas Reed A C Siemieniuk Michael Walsh Lawrie Frere David J Tunnicliffe Evi V Nagler Veena Manja Bjφrn Olav Asvold Vivekanand Jha Mieke Vermandere Karim Gariani Qian Zhao Yan Ren Emma Jane Cartwright Patrick Gee Alan Wickes Linda Fems Robin Wright Ling Li Qiukui Hao Reem A Mustafa 无 郭鹤鸣(译) | 2021 | 英国医学杂志中文版2021，24，9： | 7 |
| 3 | A Comprehensive Re- view of Hypertension in Pregnancy显示文摘 | Reem Mustafa Sana Ahmed Anu Gupta | 2012 | Published online2012，，： | 1 |
| 4 | 血浆置换和糖皮质激素治疗抗中性粒细胞胞质抗体相关性血管炎:临床实践指南显示文摘对于抗中性粒细胞胞质抗体(antineutrophil cytoplasmic antibody,ANCA)相关性血管炎(ANCA-associated vasculitis,AAV)患者,血浆置换的作用是什么?接受治疗前6个月糖皮质激素的最佳剂量是多少?一项新发表的随机对照试验(randomised controlled trial,RCT)引起本次指南的制订。 | 曾力楠 Michael Walsh Gordon H Guyatt Reed A C Siemieniuk David Collister Michelle Booth Paul Brown Lesha Farrar Mark Farrar Tracy Firth Lynn A Fussner Karin Kilian Mark A Little Thomas A Mavrakanas Reem A Mustafa Maryam Piram Lisa K Stamp 肖瑛琪 Lyubov Lytyn Thomas Agoritsas Per O Vandvik Alfred Mahr 刘峥(译) 徐佩佩(译) 张伶俐(校) | 2022 | 英国医学杂志中文版2022，25，12： | 1 |
| 5 | Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications显示文摘In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes.Microarray data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern classification.This paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics applications.The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale.Besides,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical data.Moreover,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)algorithm.The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes.For examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark datasets.The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures. | Areej A.Malibari Reem M.Alshehri Fahd N.Al-Wesabi Noha Negm Mesfer Al Duhayyim Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 6 | Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images显示文摘Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases.Magnetic resonance imaging(MRI)is a widely utilized tool for the classification and detection of prostate cancer.Since the manual screening process of prostate cancer is difficult,automated diagnostic methods become essential.This study develops a novel Deep Learning based Prostate Cancer Classification(DTL-PSCC)model using MRI images.The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors.In addition,the fuzzy k-nearest neighbour(FKNN)model is utilized for classification process where the class labels are allotted to the input MRI images.Moreover,the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm(KHA)which results in improved classification performance.In order to demonstrate the good classification outcome of the DTL-PSCC technique,a wide range of simulations take place on benchmark MRI datasets.The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%. | Areej A.Malibari Reem Alshahrani Fahd N.Al-Wesabi Siwar Ben Haj Hassine Mimouna Abdullah Alkhonaini Anwer Mustafa Hilal | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 7 | Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data显示文摘Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making process.The latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification models.At the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality problem.For resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(FSS-AICBD)technique.The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results.Primarily,the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity.In addition,the information gain(IG)approach is applied for the optimal selection of feature subsets.Also,group search optimizer(GSO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm.The choice of IG and GSO approaches results in promising medical data classification results.The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets.The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures. | Jaber S.Alzahrani Reem M.Alshehri Mohammad Alamgeer Anwer Mustafa Hilal Abdelwahed Motwakel Ishfaq Yaseen | 2022 | Computers, Materials & Continua2022，，9： | 0 |
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