OBJECTIVE: Guideline adherence in radiotherapy is crucial for maintaining treatment quality and consistency, particularly in non-trial patient settings where most treatments occur. The study aimed to assess the impact of guideline changes on treatment planning practices and compare manual registry data accuracy with treatment planning data.
METHODS: This study utilised the DBCG RT Nation cohort, a collection of breast cancer radiotherapy data in Denmark, to evaluate adherence to guidelines from 2008 to 2016. The cohort included 7448 high-risk breast cancer patients. National guideline changes included, fractionation, introduction of respiratory gating, irradiation of the internal mammary lymph nodes, use of the simultaneous integrated boost technique and inclusion of the Left Anterior Descending coronary artery in delineation practice. Methods for structure name mapping, laterality detection, detection of temporal changes in population mean lung volume, and dose evaluation were presented and applied. Manually registered treatment characteristic data was obtained from the Danish Breast Cancer Database for comparison.
RESULTS: The study found immediate and consistent adherence to guideline changes across Danish radiotherapy centres. Treatment practices before guideline implementation were documented and showed a variation among centres. Discrepancies between manual registry data and actual treatment planning data were as high as 10% for some measures.
CONCLUSIONS: National guideline changes could be detected in the routine treatment data, with a high degree of compliance and short implementation time. Data extracted from treatment planning data files provides a more accurate and detailed characterisation of treatments and guideline adherence than medical register data.

Changes in digital technology, increasing volume of data collection, and advances in methods have the potential to unleash the value of big data generated through the education of health professionals. Coupled with this potential are legitimate concerns about how data can be used or misused in ways that limit autonomy, equity, or harm stakeholders. This consensus statement is intended to address these issues by foregrounding the ethical imperatives for engaging with big data as well as the potential risks and challenges. Recognizing the wide and ever evolving scope of big data scholarship, we focus on foundational issues for framing and engaging in research. We ground our recommendations in the context of big data created through data sharing across and within the stages of the continuum of the education and training of health professionals. Ultimately, the goal of this statement is to support a culture of trust and quality for big data research to deliver on its promises for health professions education (HPE) and the health of society. Based on expert consensus and review of the literature, we report 19 recommendations in (1) framing scholarship and research through research, (2) considering unique ethical practices, (3) governance of data sharing collaborations that engage stakeholders, (4) data sharing processes best practices, (5) the importance of knowledge translation, and (6) advancing the quality of scholarship through multidisciplinary collaboration. The recommendations were modified and refined based on feedback from the 2022 Ottawa Conference attendees and subsequent public engagement. Adoption of these recommendations can help HPE scholars share data ethically and engage in high impact big data scholarship, which in turn can help the field meet the ultimate goal: high-quality education that leads to high-quality healthcare.

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts\' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

With the development and progress of science and technology, the diagnosis and treatment of intensive care medicine has incorporated monitoring tools and treatment methods. These multi-source monitoring data have the characteristics of large-scale, multi-heterogeneous, variably dynamic, high-speed and real-time acquisition, which are relatively underutilized in current clinical treatment. As the state of illness of critically ill patients changes rapidly, the timely processing and integration of multiple data becomes crucial. Therefore, intensive care big data has emerged in recent years. In order to promote the standardized development of intensive care big data in China, based on the relevant literature and guidelines at home and abroad, the Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society organized experts to discuss the concept, significance and necessity of intensive care big data; clinical scientific issues of clinical research on intensive care big data; the establishment, standards and principles of intensive care big database; ways and methods to solve big data problems in intensive care medicine; clinical application of intensive care big data. This consensus was developed for clinicians and researchers working on big data in intensive care.
随着科技的发展与进步，重症医学的诊疗融入了越来越多的监测手段与治疗方法。这些多元化的监测数据具有实时性、连续性、动态性、多源性等特点，当前临床诊疗对其利用程度相对较低。重症患者病情瞬息万变，对多元数据的及时处理与整合变得至关重要。因此，近年来重症大数据应运而生。为了更好推动重症大数据在中国的规范化发展，在借鉴国内外相关文献和指南的基础上，中国卫生信息与健康医疗大数据学会重症医学分会组织专家就本共识包括重症大数据的概念、意义与必要性；重症大数据临床研究关注的临床科学问题；重症大数据库的建立、标准与原则；重症医学大数据问题解决途径与方法；重症大数据的临床应用五个方面制定了本共识，为临床医生及致力于重症大数据的科研工作者提供参考。.

Cancer is a public health and social problem to which great attention should be attached. Not only are there a large number of cancer patients in China, but great differences also exist in etiology, epidemiology, disease spectrum, and treatment between China and western countries. Therefore, tumor-related data in China has its own characteristics. Full reference to the data of western countries cannot accurately reflect the real situation of cancer prevention and treatment in China. If we can integrate, process and analyze Chinese data, and find rules in specific etiology, incidence, drug sensitivity and prognosis, it will play an important role in the formulation of health policy, medical research and disease prevention. The Society of Cancer Big Data and Real World Study of China Anti-Cancer Association organized multidisciplinary experts, combined with domestic and foreign literature and clinical practice, after repeated discussion and revision, finished this consensus including background, analysis and management, direction planning and operation flow, basic design, quality control standards, evidence level classification, data security and privacy standards of big data and real world study. The aim is to take full advantages of Chinese cancer big data to carry out high-quality real world study, and better promote the prevention and treatment of cancer in China.
肿瘤是需要高度重视的公共卫生问题及社会问题。中国肿瘤患者不仅数量众多，而且在病因、流行病学、疾病谱、治疗方式等方面与西方国家存在较大差异。因此，中国肿瘤相关数据具有自己的特点，如果完全参照西方国家数据，则不能正确反映中国肿瘤防治的真实现状。若能整合、处理、分析中国数据，并在特有病因、发病率、药物敏感性、预后等方面找到规律，将对制定卫生政策、医学研究、疾病预防等起到重大作用。中国抗癌协会肿瘤大数据与真实世界研究专业委员会组织多学科专家，结合国内外文献和临床实践，经过反复讨论修改，基于肿瘤大数据与真实世界研究的背景、分析与管理、方向规划与操作流程、基本设计、质量控制标准、证据级别分类、数据安全与隐私标准等方面，最终形成《肿瘤大数据与真实世界研究中国专家共识(2022版)》，旨在发挥中国肿瘤大数据的优势，开展高质量的真实世界研究，更好地推进中国肿瘤防治工作。.

Spectrum clustering is a powerful strategy to minimize redundant mass spectra by grouping them based on similarity, with the aim of forming groups of mass spectra from the same repeatedly measured analytes. Each such group of near-identical spectra can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public data sets using two different clustering algorithms (spectra-cluster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for data sets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.

Publicly available compound and bioactivity databases provide an essential basis for data-driven applications in life-science research and drug design. By analyzing several bioactivity repositories, we discovered differences in compound and target coverage advocating the combined use of data from multiple sources. Using data from ChEMBL, PubChem, IUPHAR/BPS, BindingDB, and Probes & Drugs, we assembled a consensus dataset focusing on small molecules with bioactivity on human macromolecular targets. This allowed an improved coverage of compound space and targets, and an automated comparison and curation of structural and bioactivity data to reveal potentially erroneous entries and increase confidence. The consensus dataset comprised of more than 1.1 million compounds with over 10.9 million bioactivity data points with annotations on assay type and bioactivity confidence, providing a useful ensemble for computational applications in drug design and chemogenomics.

Cardiovascular diseases represent a major cause of morbidity and mortality, necessitating research to improve diagnostics, and to discover and test novel preventive and curative therapies, all of which warrant experimental models that recapitulate human disease. The translation of basic science results to clinical practice is a challenging task, in particular for complex conditions such as cardiovascular diseases, which often result from multiple risk factors and comorbidities. This difficulty might lead some individuals to question the value of animal research, citing the translational \'valley of death\', which largely reflects the fact that studies in rodents are difficult to translate to humans. This is also influenced by the fact that new, human-derived in vitro models can recapitulate aspects of disease processes. However, it would be a mistake to think that animal models do not represent a vital step in the translational pathway as they do provide important pathophysiological insights into disease mechanisms particularly on an organ and systemic level. While stem cell-derived human models have the potential to become key in testing toxicity and effectiveness of new drugs, we need to be realistic, and carefully validate all new human-like disease models. In this position paper, we highlight recent advances in trying to reduce the number of animals for cardiovascular research ranging from stem cell-derived models to in situ modelling of heart properties, bioinformatic models based on large datasets, and state-of-the-art animal models, which show clinically relevant characteristics observed in patients with a cardiovascular disease. We aim to provide a guide to help researchers in their experimental design to translate bench findings to clinical routine taking the replacement, reduction, and refinement (3R) as a guiding concept.

BACKGROUND: Living Labs are user-centered, open innovation ecosystems based on a systematic user cocreation approach, which integrates research and innovation processes in real-life communities and settings. The Horizon 2020 Project VITALISE (Virtual Health and Wellbeing Living Lab Infrastructure) unites 19 partners across 11 countries. The project aims to harmonize Living Lab procedures and enable effective and convenient transnational and virtual access to key European health and well-being research infrastructures, which are governed by Living Labs. The VITALISE consortium will conduct joint research activities in the fields included in the care pathway of patients: rehabilitation, transitional care, and everyday living environments for older adults. This protocol focuses on health and well-being research in everyday living environments.
OBJECTIVE: The main aim of this study is to cocreate and test a harmonized research protocol for developing big data-driven hybrid persona, which are hypothetical user archetypes created to represent a user community. In addition, the use and applicability of innovative technologies will be investigated in the context of various everyday living and Living Lab environments.
METHODS: In phase 1, surveys and structured interviews will be used to identify the most suitable Living Lab methods, tools, and instruments for health-related research among VITALISE project Living Labs (N=10). A series of web-based cocreation workshops and iterative cowriting processes will be applied to define the initial protocols. In phase 2, five small-scale case studies will be conducted to test the cocreated research protocols in various real-life everyday living settings and Living Lab infrastructures. In phase 3, a cross-case analysis grounded on semistructured interviews will be conducted to identify the challenges and benefits of using the proposed research protocols. Furthermore, a series of cocreation workshops and the consensus seeking Delphi study process will be conducted in parallel to cocreate and validate the acceptance of the defined harmonized research protocols among wider Living Lab communities.
RESULTS: As of September 30, 2021, project deliverables Ethics and safety manual and Living lab standard version 1 have been submitted to the European Commission review process. The study will be finished by March 2024.
CONCLUSIONS: The outcome of this research will lead to harmonized procedures and protocols in the context of big data-driven hybrid persona development among health and well-being Living Labs in Europe and beyond. Harmonized protocols enable Living Labs to exploit similar research protocols, devices, hardware, and software for interventions and complex data collection purposes. Economies of scale and improved use of resources will speed up and improve research quality and offer novel possibilities for open data sharing, multidisciplinary research, and comparative studies beyond current practices. Case studies will also provide novel insights for implementing innovative technologies in the context of everyday Living Lab research.
UNASSIGNED: DERR1-10.2196/34567.

The use of big data containing millions of primary care medical records provides an opportunity for rapid research to help inform patient care and policy decisions during the first and subsequent waves of the coronavirus disease 2019 (COVID-19) pandemic. Routinely collected primary care data have previously been used for national pandemic surveillance, quantifying associations between exposures and outcomes, identifying high risk populations, and examining the effects of interventions at scale, but there is no consensus on how to effectively conduct or report these data for COVID-19 research. A COVID-19 primary care database consortium was established in April 2020 and its researchers have ongoing COVID-19 projects in overlapping data sets with over 40 million primary care records in the United Kingdom that are variously linked to public health, secondary care, and vital status records. This consensus agreement is aimed at facilitating transparency and rigor in methodological approaches, and consistency in defining and reporting cases, exposures, confounders, stratification variables, and outcomes in relation to the pharmacoepidemiology of COVID-19. This will facilitate comparison, validation, and meta-analyses of research during and after the pandemic.