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Development of a generalised tool for evaluating success of clinical practice guidelines implementation (A-GIST). 开发用于评估临床实践指南实施成功的通用工具(a - gist)。
IF 7.6 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Pub Date : 2025-07-21 DOI: 10.1136/bmjebm-2024-113308
Kun Zou, Nan Yang, Siyi He, Linan Zeng, Hailong Li, Liang Huang, Qiusha Yi, Xiangyu Gao, Jiajun Ren, Mingyue Zhang, Qiang Wang, Lingli Zhang

Objectives: To develop a generalised tool to evaluate the success of implementation of clinical practice guidelines (CPGs).

Design and setting: Systematic review, group brainstorming discussion and modified Delphi method.

Participants: A steering group (3 members) was assembled responsible for the guidance and ensuring stakeholder's involvement. A tool development group (24 members) of leading experts provided expertise in refining the frame and items during the development and participated in the Delphi process. And a secretary group (7 members) was responsible for the organization and coordination, systematic reviewing, drafting of the preliminary list of items, documenting and revising the tool according to the suggestions of the development group.

Interventions: Four process stages were employed. First, the project was launched with core groups formed for the development. Second, based on the Reach Effectiveness Adoption Implementation Maintenance framework, a systematic review of existing methods for evaluating the success of CPGs implementation and a brainstorming discussion were conducted to form a preliminary list. Third, a modified Delphi method was organised, integrating a multidisciplinary face-to-face consultation meeting with two rounds of online Delphi consultations. Fourth, the tool was revised and finalised incorporating all expert suggestions.

Results: Development teams comprising multidisciplinary experts were formed. In the systematic review, 7 biomedical literature databases were searched, and 208 pieces of literature were included. After three rounds of brainstorming discussions on items identified in the literature, a 23-item preliminary list was produced. In the modified Delphi method, 14 experts made 23 main suggestions in the face-to-face meeting on the list. 13 experts participated in the first round of Delphi consultation, reached agreement on 20 items, removed 4 items and added 1 new item. 11 experts attended the second-round consultation and had an agreement to include 20 items after revision.

Conclusions: The Guideline Implementation Success Assessment Tool (A-GIST) was systematically developed as a comprehensive tool to assess the success of CPGs implementation. It comprises 5 dimensions, Reach, Adoption, Implementation, Effectiveness and Maintenance, encompassing 20 items that integrate perspectives from both clinicians and patients. A-GIST is designed to facilitate evaluating, benchmarking and cross-comparison of implementation success between guidelines, facilities and regions. Additionally, it provides evidence-based insights to inform the development of targeted strategies for optimising guideline implementation practices.

目的:开发一种通用的工具来评估临床实践指南(CPGs)的成功实施。设计与设置:系统回顾,小组头脑风暴讨论,修正德尔菲法。参与者:组建了一个指导小组(3名成员),负责指导和确保涉众的参与。由主要专家组成的工具开发小组(24名成员)在开发过程中提供了细化框架和项目的专业知识,并参与了德尔菲过程。由秘书组(7人)负责组织协调、系统审查、起草初步项目清单,并根据开发组的建议对工具进行整理和修订。干预措施:采用四个过程阶段。首先,项目启动,形成核心开发小组。其次,基于Reach Effectiveness Adoption Implementation Maintenance framework,对现有CPGs实施成功评估方法进行系统回顾,并进行头脑风暴讨论,形成初步清单。第三,组织了一种改进的德尔菲法,将多学科面对面咨询会议与两轮在线德尔菲咨询相结合。第四,对该工具进行了修订并最终确定,纳入了所有专家建议。结果:形成了由多学科专家组成的开发团队。本系统综述检索了7个生物医学文献数据库,纳入文献208篇。在对文献中确定的项目进行了三轮头脑风暴讨论之后,产生了一份23个项目的初步清单。在改进的德尔菲法中,14位专家在清单上的面对面会议中提出了23条主要建议。13位专家参加了第一轮德尔菲咨询,就20个项目达成一致,删除4个项目,新增1个项目。11名专家参加了第2次协商,并决定修改后增加20个项目。结论:指南实施成功评估工具(a - gist)被系统地开发为评估CPGs实施成功的综合工具。它包括5个维度,即Reach、采用、实施、有效性和维护,包括20个项目,整合了临床医生和患者的观点。A-GIST的目的是促进对准则、设施和区域之间实施成功情况的评价、基准和交叉比较。此外,它提供了基于证据的见解,为优化指南实施实践的目标战略的发展提供信息。
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引用次数: 0
Ending nuclear weapons, before they end us. 在核武器终结我们之前终结它们。
IF 7.6 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Pub Date : 2025-07-21 DOI: 10.1136/bmjebm-2025-113922
Chris Zielinski
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引用次数: 0
Incomplete reporting and spin in acupuncture randomised controlled trials: a cross-sectional meta-epidemiological study. 针灸随机对照试验的不完整报告和自旋:一项横断面荟萃流行病学研究。
IF 7.6 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Pub Date : 2025-07-21 DOI: 10.1136/bmjebm-2024-113364
Yuting Duan, Pinge Zhao, Yuening Deng, Zhirui Xu, Siqi Wu, Lin Shi, Feng Jiang, Shujuan Liu, Xinyu Li, Binbin Tang, Jinjin Zhou, Lin Yu

Objectives: To investigate the reporting, data sharing and spin (using reporting strategies to emphasise the benefit of non-significant results) in acupuncture randomised controlled trials (RCTs).

Design: Cross-sectional meta-epidemiological study.

Data sources: Eligible studies indexed in MEDLINE, Embase, CENTRAL, CBM, CNKI, Wanfang Data and VIP Database between 1 January 2014 and 1 May 2024.

Eligibility criteria: Peer-reviewed acupuncture RCTs used traditional medicine (TM), published in English or Chinese, two parallel arms for humans.

Main outcome measures: We assessed (1) the reporting of acupuncture RCTs by the Consolidated Standards for Reporting Trials (CONSORT) 2010 statement and STandards for Reporting Interventions in Clinical Trials of Acupuncture (STRICTA) checklist; (2) the data sharing level by the International Committee of Medical Journal Editors (ICMJE) data sharing statement; (3) spin frequency and level by the prespecified spin strategies.

Results: This study evaluated 476 eligible studies, of which 166 (34.9%) explored the specific efficacy or safety of acupuncture in the nervous system, 68 (14.3%) in the motor system and 61 (12.8%) in the digestive system. 244 (57.7%) studies used conventional acupuncture, 296 (62.2%) used multicentre study design and 369 (77.5%) were supported by institutional funding. 312 (65.5%) eligible studies were poorly reported. The sufficiently reporting scores of the CONSORT 2010 statement and the STRICTA checklist differed from 0.63% to 97.5%, and 32 (59.3%) items were less than 50%. For the data sharing level of acupuncture RCTs, only 66 (17.2%) studies followed the ICMJE data sharing statement, but 49 (14.5%) need to require authors to obtain data, and only 5 (1.5%) provided data by open access. Spins were identified in 408 (85.7%) studies (average spin frequencies: 2.94). 59 (37.2%) studies with non-significant primary outcomes had spin levels.

Conclusions: This study found that the reporting of acupuncture RCTs was low compliance with the CONSORT 2010 statement, the STRICTA checklist and the ICMJE data sharing statement, and spin appeared frequently. Journal policies on using reporting guidelines, data sharing and equitable consideration of non-significant results might enhance the reporting of acupuncture RCTs.

Trial registration number: This study was registered at the Open Science Framework (OSF): (https://doi.org/10.17605/OSF.IO/2WTE6, and https://doi.org/10.17605/OSF.IO/9XDN4,).

目的:调查针灸随机对照试验(RCTs)中的报告、数据共享和spin(使用报告策略来强调非显著结果的益处)。设计:横断面荟萃流行病学研究。数据来源:2014年1月1日至2024年5月1日在MEDLINE、Embase、CENTRAL、CBM、CNKI、万方数据和VIP数据库中检索的符合条件的研究。入选标准:同行评议的针灸随机对照试验使用传统医学(TM),以英文或中文发表,人类两组平行。主要结局指标:我们评估(1)针灸随机对照试验报告综合标准(CONSORT) 2010声明和针灸临床试验干预报告标准(STRICTA)清单;(2)国际医学期刊编辑委员会(ICMJE)数据共享声明的数据共享水平;(3)自旋频率和水平由预先设定的自旋策略决定。结果:本研究共评估了476项符合条件的研究,其中166项(34.9%)研究探讨了针刺对神经系统的特异性疗效或安全性,68项(14.3%)研究探讨了运动系统,61项(12.8%)研究探讨了消化系统。244项(57.7%)研究采用传统针灸,296项(62.2%)采用多中心研究设计,369项(77.5%)采用机构资助。312项(65.5%)符合条件的研究报告不足。CONSORT 2010声明和STRICTA检查表的充分报告得分从0.63%到97.5%不等,有32项(59.3%)低于50%。在针灸rct的数据共享水平上,只有66项(17.2%)研究遵循了ICMJE数据共享声明,但49项(14.5%)研究需要作者获取数据,只有5项(1.5%)研究采用开放获取方式提供数据。自旋在408项(85.7%)研究中被发现(平均自旋频率:2.94)。59项(37.2%)无显著主要结局的研究有自旋水平。结论:本研究发现针灸rct报告对CONSORT 2010声明、STRICTA清单和ICMJE数据共享声明的符合性较低,spin频繁出现。期刊关于使用报告指南、数据共享和公平考虑非显著结果的政策可能会加强针灸随机对照试验的报告。试验注册号:本研究在开放科学框架(OSF)注册:(https://doi.org/10.17605/OSF.IO/2WTE6, and https://doi.org/10.17605/OSF.IO/9XDN4,)。
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引用次数: 0
Rapid reviews methods series: guidance on rapid scoping, mapping and evidence and gap map ('Big Picture Reviews'). 快速审查方法系列(文件7):关于快速范围界定、制图、证据和差距图的指南(“大局审查”)。
IF 7.6 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Pub Date : 2025-07-21 DOI: 10.1136/bmjebm-2023-112389
Fiona Campbell, Anthea Sutton, Danielle Pollock, Chantelle Garritty, Andrea C Tricco, Lena Schmidt, Hanan Khalil
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引用次数: 0
Co-production and implementation of an evidence collation strategy for a novel point-of-care information resource: gpevidence.org. 共同制作和实施一个新的即时护理信息资源证据整理策略:gevidence。
IF 9 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Pub Date : 2025-07-18 DOI: 10.1136/bmjebm-2025-113715
Julian Treadwell, Laura C Armitage, Kiana K Collins, Laura Heath, Olu Onyimadu, Mandy Payne, Azizia Wahedi, Kamal R Mahtani
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引用次数: 0
Understanding synthetic data: artificial datasets for real-world evidence. 理解合成数据:真实世界证据的人工数据集。
IF 9 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Pub Date : 2025-07-02 DOI: 10.1136/bmjebm-2024-113617
Randi Foraker, Jon D Morrow, Julie A Johnson, Adam B Wilcox, Alan J Forster, Philip R O Payne
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引用次数: 0
CARE extension guideline for acupuncture case reports. CARE扩展针灸病例报告指南。
IF 9 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Pub Date : 2025-07-01 DOI: 10.1136/bmjebm-2025-113641
Yuting Duan, Zhirui Xu, Yongqi Zhang, Shujuan Liu, Juexuan Chen, Yaolong Chen, Nenggui Xu, Chunzhi Tang, Peijing Rong, Liming Lu, Yu Wang, Ye-Seul Lee, Tae-Hun Kim, David S Riley, Lin Shi, Myeong Soo Lee, Lin Yu

Existing reporting checklists lack the specificity and comprehensiveness required to effectively guide the documentation of acupuncture case reports. Therefore, we developed a reporting guideline tailored specifically for acupuncture case reports, building upon the CAse REport (CARE) statement. A multidisciplinary group of international experts including clinicians, researchers and methodologists was convened to draft the initial checklist in accordance with the methodology recommended by the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) network. Through an extensive literature review and a series of expert interviews, the final CARE for acupuncture checklist comprised of 30 items. 38 experts from diverse disciplines participated in three rounds of modified Delphi surveys to refine and clarify these items. CARE for acupuncture is a comprehensive reporting guideline focused on acupuncture case reports developed with rigorous methodology. We hope that CARE for acupuncture will further guide authors, editors, peer reviewers and readers to enhance the transparency, completeness and accuracy of reporting of case reports in acupuncture.

现有的报告清单缺乏特异性和全面性,无法有效地指导针灸病例报告的记录。因此,我们在病例报告(CARE)声明的基础上,制定了专门针对针灸病例报告的报告指南。召集了一个包括临床医生、研究人员和方法学家在内的多学科国际专家小组,根据提高卫生研究质量和透明度(EQUATOR)网络建议的方法起草初步核对表。通过广泛的文献回顾和一系列专家访谈,最终的CARE for acupuncture checklist包括30个项目。38名来自不同学科的专家参与了三轮修改的德尔菲调查,以完善和澄清这些项目。CARE for acupuncture是一个综合性的针灸病例报告指南,以严谨的方法开发。我们希望《CARE for acupuncture》能进一步指导作者、编辑、同行评审和读者提高针灸病例报告的透明度、完整性和准确性。
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引用次数: 0
Accurate is not enough: select formats for communicating probabilities to achieve specific outcomes. 准确是不够的:选择沟通概率的格式来实现特定的结果。
IF 7.6 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Pub Date : 2025-07-01 DOI: 10.1136/bmjebm-2025-113738
Brian J Zikmund-Fisher, Jessica S Ancker
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引用次数: 0
Romanticised semiology. 浪漫化符号学。
IF 9 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Pub Date : 2025-05-28 DOI: 10.1136/bmjebm-2025-113664
José Nunes de Alencar, Pedro Henrique Brandão da Silva, Vitor Borin
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引用次数: 0
What makes a 'good' decision with artificial intelligence? A grounded theory study in paediatric care. 如何用人工智能做出“好的”决策?在儿科护理接地理论研究。
IF 9 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Pub Date : 2025-05-20 DOI: 10.1136/bmjebm-2024-112919
Melissa D McCradden, Kelly Thai, Azadeh Assadi, Sana Tonekaboni, Ian Stedman, Shalmali Joshi, Minfan Zhang, Fanny Chevalier, Anna Goldenberg
<p><strong>Objective: </strong>To develop a framework for good clinical decision-making using machine learning (ML) models for interventional, patient-level decisions.</p><p><strong>Design: </strong>Grounded theory qualitative interview study.</p><p><strong>Setting: </strong>Primarily single-site at a major urban academic paediatric hospital, with external sampling.</p><p><strong>Participants: </strong>Sixteen participants representing physicians (n=10), nursing (n=3), respiratory therapists (n=2) and an ML specialist (n=1) with experience working in acute care environments were identified through purposive sampling. Individuals were recruited to represent a spectrum of ML knowledge (three expert, four knowledgeable and nine non-expert) and years of experience (median=12.9 years postgraduation). Recruitment proceeded through snowball sampling, with individuals approached to represent a diversity of fields, levels of experience and attitudes towards artificial intelligence (AI)/ML. A member check step and consultation with patients was undertaken to vet the framework, which resulted in some minor revisions to the wording and framing.</p><p><strong>Interventions: </strong>A semi-structured virtual interview simulating an intensive care unit handover for a hypothetical patient case using a simulated ML model and seven visualisations using known methods addressing interpretability of models in healthcare. Participants were asked to make an initial care plan for the patient, then were presented with a model prediction followed by the seven visualisations to explore their judgement and potential influence and understanding of the visualisations. Two visualisations contained contradicting information to probe participants' resolution process for the contrasting information. The ethical justifiability and clinical reasoning process were explored.</p><p><strong>Main outcome: </strong>A comprehensive framework was developed that is grounded in established medicolegal and ethical standards and accounts for the incorporation of inference from ML models.</p><p><strong>Results: </strong>We found that for making good decisions, participants reflected across six main categories: evidence, facts and medical knowledge relevant to the patient's condition; how that knowledge may be applied to this particular patient; patient-level, family-specific and local factors; facts about the model, its development and testing; the patient-level knowledge sufficiently represented by the model; the model's incorporation of relevant contextual factors. This judgement was centred on and anchored most heavily on the overall balance of benefits and risks to the patient, framed by the goals of care. We found evidence of automation bias, with many participants assuming that if the model's explanation conflicted with their prior knowledge that their judgement was incorrect; others concluded the exact opposite, drawing from their medical knowledge base to reject the incorrect informati
目的:利用机器学习(ML)模型开发一个良好的临床决策框架,用于介入,患者层面的决策。设计:扎根理论定性访谈研究。环境:主要是在一个主要的城市学术儿科医院的单一站点,有外部抽样。参与者:16名参与者代表医生(n=10),护理(n=3),呼吸治疗师(n=2)和ML专家(n=1),通过有目的的抽样确定在急性护理环境中工作的经验。被招募的个人代表了机器学习知识(3名专家,4名知识渊博者和9名非专家)和经验(毕业后中位数=12.9年)的范围。招聘通过滚雪球抽样进行,每个人都代表不同的领域、经验水平和对人工智能(AI)/ML的态度。成员检查步骤和咨询患者进行审查框架,这导致了一些小的修改措辞和框架。干预措施:一个半结构化的虚拟访谈模拟重症监护病房的交接,使用模拟的ML模型和七个可视化使用已知的方法解决医疗保健模型的可解释性。参与者被要求为病人制定一个初步的护理计划,然后向他们展示一个模型预测,然后是七个可视化图像,以探索他们对可视化图像的判断、潜在影响和理解。两个可视化图像包含矛盾的信息,以探索参与者对对比信息的解决过程。探讨了伦理正当性和临床推理过程。主要成果:开发了一个全面的框架,该框架以已建立的医学和伦理标准为基础,并考虑了机器学习模型的推断。结果:我们发现,为了做出好的决定,参与者反映了六个主要类别:与患者病情相关的证据、事实和医学知识;如何将这些知识应用到这个病人身上;患者层面、家庭特定和地方因素;关于模型、开发和测试的事实;由模型充分表示的患者层面的知识;该模型纳入了相关的语境因素。这一判断主要集中在对患者的利益和风险的总体平衡上,并以护理目标为框架。我们发现了自动化偏差的证据,许多参与者认为,如果模型的解释与他们的先验知识相冲突,他们的判断是不正确的;另一些人则得出了完全相反的结论,他们从自己的医学知识基础出发,拒绝了解释中提供的不正确信息。关于模型的知识,我们发现参与者最一致地想知道模型在假设患者所在的当地单位的患者队列中的历史表现。结论:使用人工智能工具做出好的决策需要跨多个领域的反思。我们提供了一个可操作的框架和问题指南,以支持人工智能的临床决策。
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引用次数: 0
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BMJ Evidence-Based Medicine
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