Transparent Process Research Articles

Enhancing environmental sustainability through lean construction principles: an industry-based SEM analysis

PurposeIn the modern era, the concept of sustainability has become pervasive, permeating various sectors, including the construction industry. Driven by growing environmental concerns, construction organizations are increasingly compelled to seek operational excellence while realigning their practices towards environmental sustainability goals. Hence, this paper investigates the capability of lean construction (LC) principles in enhancing environmental performance (EP) within the construction industry using a structural equation modeling (SEM) approach.Design/methodology/approachThe study employs SEM to explore the relationship between LC components and environmental performance factors in the construction context. This methodology integrates both empirical and qualitative analyses within a structured framework to explore the relationship between LC components and EP factors in the construction context. By collecting and statistically testing data from 307 Moroccan construction companies, the study evaluates the impact of five LC principles – process transparency and standardization, continuous improvement, waste elimination, planning and scheduling and material flow and pull – on four environmental performance indicators: material use, energy consumption, non-product output and pollutant releases.FindingsThe analysis reveals that, among the five LC principles – process transparency and standardization, continuous improvement, waste elimination, planning and scheduling and material flow and pull – the principles of waste elimination and planning and scheduling significantly contribute to environmental improvements. These practices were shown to impact positively four environmental performance indicators: material use, energy consumption, non-product output and pollutant releases.Originality/valueThis research fills a critical gap in empirical studies on the beneficial effects of LC practices on environmental sustainability in the construction industry. It provides theoretical insights and practical recommendations for managers and practitioners to effectively implement LC principles, thereby promoting more efficient and sustainable practices, particularly in developing countries.

Enhancing intention prediction and interpretability in service robots with LLM and KG.

The rapid advancement of artificial intelligence has significantly expanded the role of service robots in everyday life. This expansion necessitates the accurate recognition and prediction of human intentions to provide timely and appropriate services. However, existing methods often struggle to perform effectively in complex and unstructured environments. To address this challenge, we propose the Large language model and Knowledge graph based Intention Recognition Framework (LKIRF), which combines large language model (LLM) with knowledge graphs (KG) to enhance the intention recognition capabilities of service robots. Our approach constructs an offline KG from human motion and environmental data and builds an online reasoning graph through real-time interaction, utilizing LLM for interpretation. Experimental results indicate that compared to traditional methods, LKIRF not only improves prediction accuracy across various scenarios but also enhances the transparency and interpretability of the intention reasoning process.

Enhancing Structured Query Language Injection Detection with Trustworthy Ensemble Learning and Boosting Models Using Local Explanation Techniques

This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables attackers to exploit databases, gain unauthorized access, and manipulate data. Traditional detection methods often struggle due to the constantly evolving nature of these attacks, the increasing complexity of modern web applications, and the lack of transparency in the decision-making processes of machine learning models. To address these challenges, we evaluated the performance of various models, including decision tree, random forest, XGBoost, AdaBoost, Gradient Boosting Decision Tree (GBDT), and Histogram Gradient Boosting Decision Tree (HGBDT), using a comprehensive SQL injection dataset. The primary motivation behind our approach is to leverage the strengths of ensemble learning and boosting techniques to enhance detection accuracy and robustness against SQL injection attacks. By systematically comparing these models, we aim to identify the most effective algorithms for SQL injection detection systems. Our experiments show that decision tree, random forest, and AdaBoost achieved the highest performance, with an accuracy of 99.50% and an F1 score of 99.33%. Additionally, we applied SHapley Additive exPlanations (SHAPs) and Local Interpretable Model-agnostic Explanations (LIMEs) for local explainability, illustrating how each model classifies normal and attack cases. This transparency enhances the trustworthiness of our approach to detecting SQL injection attacks. These findings highlight the potential of ensemble methods to provide reliable and efficient solutions for detecting SQL injection attacks, thereby improving the security of web applications.

Patient and public involvement in the design of an international clinical trial: real world experience.

The value of patient and public involvement (PPI) during the earliest stages of clinical trial development, and prior to the award of substantive funding, is widely recognised. However, it is often under-resourced and PPI processes during this phase are rarely reported in detail. Having benefitted from seed funding to develop an international clinical trial proposal, we sought to describe and appraise PPI activities and processes that support pre-award co-development. A 12-month "accelerator" award facilitated development of a substantive funding application to deliver the Rheumatoid Arthritis Prevention PlatfORm Trial (RAPPORT), conceived to prioritise preventative interventions for people at risk of RA. PPI partners, including individuals at risk of rheumatoid arthritis (RA), RA patients, relatives and members of the public, provided feedback on key trial design issues through online meetings, a feedback form and emails. PPI processes employed during the one-year accelerator project were thereafter evaluated by PPI partners using an anonymous online feedback form with reference to National Institute of Health and Care Research (NIHR) UK standards for public involvement in research. Sixteen out of the 25-strong PPI partner panel completed an online feedback form (64%). Respondents perceived PPI processes positively in relation to all NIHR standard domains. Several key facilitators and challenges were identified, including the need for adequate PPI funding during pre-award phases of research, strategies for creating an inclusive environment, flexibility around levels of involvement, and challenges in achieving representatively diverse participation, and the importance of communicating transparent processes for role-assignment and time-reimbursement. In general, RAPPORT was considered an example of PPI well done, and in line with UK standards for public involvement in research. Facilitators and challenges of relevance for the development of future translational and clinical trial funding applications are highlighted.

Understanding Involuntary Hospitalization Applications Submitted to an Urban Police Department.

To improve understanding of the application process for temporary involuntary hospitalization (Section 12 of the Massachusetts General Laws) in Boston, the authors focused on cases involving the Boston Police Department (PD) and the information shared about the individuals involved. A retrospective analysis was conducted on all Section 12 applications submitted to the Boston PD by external clinicians from July 14, 2021, to June 30, 2022. The authors analyzed 488 applications processed by the Boston PD's Street Outreach Unit, examining demographic information, reasons for requests, information provided by petitioners, and the status of the applications (completed vs. not). The analysis revealed racial disparities: 41% of involuntary hospitalization applications were for individuals identified as Black or African American, but this racial group represents only 23% of Boston's population. Racial-ethnic data were based on police perceptions, and 21% of cases lacked race-ethnicity data. Seventy-six percent of applications were submitted without a direct clinical examination by petitioners, who did not justify the omission. The Boston PD completed 70% of the requested involuntary hospitalization orders. This study identified substantial racial disparities in Section 12 applications, which disproportionately involved Black or African American individuals. The frequent absence of direct clinical examinations before application submissions and the lack of justification indicated a need for regulatory oversight and enhanced petitioner training. Incomplete demographic data underscored the need for improved data collection and reporting practices. These findings highlight the need for reforms to ensure equitable, transparent, and best practice-aligned involuntary hospitalization processes.

Application of artificial intelligence in career development of women in STEM field: Counseling, network construction and recruitment

With the rapid development of science and technology, the application of artificial intelligence (AI) in STEM (science, technology, engineering and mathematics) is increasingly extensive, which provides a new idea for solving the challenges of women's career development in STEM. This paper deeply discusses the application of AI in counseling, network construction and recruitment of women's STEM career development, aiming at promoting gender equality and women's career growth. In career counseling, AI technology provides customized career advice and development path for women through deep learning and big data analysis. According to personal education background, skill level, hobbies and career goals, the system intelligently recommends suitable career directions, and updates industry trends and job market data in real time to help women make more informed career choices. The AI tutoring system also enhances women's professional self-confidence through functions such as simulated interviews and career planning exercises. In terms of network construction, AI helps women to expand their professional networks more effectively through intelligent recommendation system. Based on user behavior data and preferences, AI accurately recommends industry seminars, technical exchange activities and online communities to promote women to establish contacts with like-minded professionals. AI also assists women in managing and maintaining network relationships, ensures the continuity of network interaction through intelligent reminding functions, and identifies key nodes and influential people to enhance network quality. In the recruitment process, AI technology reduces gender bias and improves the fairness and transparency of recruitment through automatic resume screening, standardized interview process and competency-based evaluation model. AI system can analyze a large amount of data, accurately match the skills and job requirements of female candidates, and improve the employment rate of women in STEM field. At the same time, through transparent algorithm decision-making process and fairness check, the potential gender bias problem in AI algorithm is overcome. The research in this paper provides theoretical support and practical guidance for using AI technology to promote women's career development in STEM field, and helps to build a more equal and inclusive scientific and technological working environment.

Navigating food security challenges: A comprehensive analysis, gaps, and future directions

In the context of contemporary global challenges such as the COVID-19 pandemic, geopolitical conflicts, and climate change, food security assumes particular significance, being an integral part of national security. This study aims to investigate the interplay between food security and national security systems, with a focus on identifying gaps in the literature and determining directions for further research. The study conducted a systematic literature review on food security and national security systems employing a rigorous and transparent process. The qualitative analysis is grounded in the quantitative one, encompassing studies from Scopus. The examination of the selected peer-reviewed articles revealed several methodological and thematic limitations in existing research: i Geographic imbalance: There is a predominant focus on developed countries, while food security issues in developing countries remain insufficiently studied; ii Insufficient explication: There is a lack of research dedicated to managerial and economic aspects of food security in the context of national security; iii Methodological constraints: There is a predominance of quantitative methods and retrospective/cross-sectional studies. Recommendations include developing comprehensive strategies at both global and national levels to enhance food stability and accessibility.

"Expensive Sisters".

This perspective highlights a growing and concerning trend within the scientific publishing community: the increasing incidence of manuscript rejections within twenty-four hours of submission without peer review, followed by offers of transfer to a sister journal with a high article processing fee. Recommendations to address such issues, including increased transparency in the manuscript review process, the establishment of more robust editorial guidelines, and the promotion of equitable publishing opportunities regardless of financial capability have also been proposed.

Exploring the Impact of Social Support on Tuberculosis Treatment: A Thematic Review

Aims: Tuberculosis (TB) remains a critical global health issue, characterized by significant morbidity and mortality rates. Recent research highlights the potential of social support interventions to enhance TB treatment outcomes and improve patient quality of life. This study examines the positive effects of social support interventions on TB patients throughout their treatment process. Method: Utilizing a traditional review approach, this study applies thematic analysis to synthesize findings from the literature. The review encompasses 17 articles selected from reputable databases, including PubMed, Wiley Online Library, ProQuest, and Google Scholar, based on predefined inclusion criteria. These articles underwent quality assessment using the Critical Appraisal Skills Programme (CASP) tool. The PRISMA flowchart provided transparency in the selection process. Results: Key themes identified through the analysis include treatment adherence, psychological well-being, motivation to recover, health-related quality of life, prevention of TB transmission, successful TB treatment, and self-efficacy to recover. Conclusion: The findings suggest that social support interventions can play a crucial role in addressing the persistent challenges of TB management globally.

Silicon rich nitride: a platform for controllable structural colors

Abstract High refractive index dielectric materials like silicon rich nitride (SRN) are critical for constructing advanced dielectric metasurfaces but are limited by transparency and complementary metal oxide semiconductor (CMOS) process compatibility. SRN’s refractive index can be adjusted by varying the silicon to nitride ratio, although this increases absorption, particularly in the blue spectrum. Dielectric metasurfaces, which utilize the material’s high dielectric constant and nano-resonator geometry, experience loss amplification due to resonance, affecting light reflection, light transmission, and quality factor. This study explores the impact of varying the silicon ratio on structural color applications in metasurfaces, using metrics such as gamut coverage, saturation, and reflection amplitude. We found that a higher SRN ratio enhances these metrics, making it ideal for producing vivid structural colors. Our results show that SRN can produce a color spectrum covering up to 166 % of the sRGB space and a resolution of 38,000 dots per inch. Fabricated samples vividly displayed a parrot, a flower, and a rainbow, illustrating SRN’s potential for high-resolution applications. We also show that SRN can provide a better CIE diagram coverage than other popular metasurfaces materials. These findings highlight the advantages of SRN for photonic devices, suggesting pathways for further material and application development.

Consumerbased conceptualisation of brand transparency: scope, characteristics and contextual determinants

Purpose Escalating uncertainty surrounding brand communications has intensified consumer demands for transparency. Many definitions link transparency to the quantity of shared information, yet more information might not alleviate consumer uncertainty. The purpose of this paper is to develop a consumer-based conceptualisation of brand transparency that recognises the subjectivity in how transparency manifests for consumers. Design/methodology/approach This research adopts a qualitative approach, leveraging 10 exploratory interviews with experts and 20 in-depth interviews with consumers. Findings Confronted with information asymmetry, consumers rely on cues (openness, clarity, timeliness, evidence-based, explanatory) to evaluate a brand’s intentions to provide accurate information about focal domains in a way that establishes brand transparency. Focal domains of brand transparency (pro-social values, processes and product and service offerings) evolve in line with changing consumer expectations. Both consumer relationships and brand experiences influence brand transparency perceptions. Research limitations/implications This study challenges an inherent assumption that access to more information informs brand transparency perceptions; instead, consumers require transparency about salient and focal topics. By delineating the observable signals consumers use to infer transparency and highlighting how consumers’ biases towards certain brands and product categories influence their perceptions of brand transparency, this study contributes to customer–brand relationship literature. Practical implications The authors identify challenges for evoking brand transparency perceptions when information is salient. The authors stress the importance of open dialogue across all touchpoints to address consumer queries. Originality/value By challenging some assumptions of brand transparency literature, which have evolved from accounting and governance disciplines, this research introduces a distinctive perspective on consumer-based brand transparency.

SCIENTIFIC PRODUCTION ON PUBLIC CONTRACT MANAGEMENT IN BRAZIL: A SYSTEMATIC REVIEW

Efficient management of public contracts is essential to ensure the proper allocation of resources and the effectiveness of operations in government institutions, including military ones. This study conducts a systematic review of the scientific literature on contract management in Brazilian military institutions between 2013 and 2023, aiming to identify trends, gaps, and research opportunities. The research employed a mixed-method approach, combining systematic review and bibliometric analysis, and analyzed articles from the SPELL database. The results show a significant increase in the number of publications in 2023, with a focus on the Southeast and Northeast regions, while the North region exhibited a notable lack of studies. Revista de Administração Pública was the most productive journal, and Eduardo José Grin the most cited researcher. The analysis also revealed a concentration of studies in public universities in the Southeast. The study suggests that adopting technological innovations, such as automation and digital tools, has the potential to enhance transparency and efficiency in public procurement processes. The lack of research on contract management in the North region underscores the need for greater academic attention in this area, creating opportunities for further studies.

Influence of Opening Time Interval of Gate Signals on Suppression of Horizontally Polarized Signal of Infrared Pulsed Laser

Since the beginning of the 21st century, infrared Nd:YVO4 pulsed lasers have been widely applied, especially in some actual industrial processes. In the working process of a laser-aided etching device, the “match-head” effect must be effectively controlled by suppressing the first giant pulse for a solid-state Q-switched laser. In the process of optimizing the infrared Nd:YVO4 pulsed laser by adjusting the slope parameters of the radio frequency (RF) modulation to suppress the first giant pulse, it has been found that an abnormal horizontally polarized emission with a very short time appears before the formal vertically polarized emission when the gate signal is artificially started. Actually, abnormal horizontally polarized emissions will bring some unexpected machining traces during the production process and even greater dangers. The experimental results show that with the increase in the slope duration of an RF signal, the existence time of abnormal output horizontally polarized light will be shortened, and the horizontal giant pulse and vertical giant pulse are well suppressed. When the slope duration is greater than 0.18 ms, both horizontal and vertical giant pulses will disappear. The horizontally polarized light can be thoroughly suppressed when the slope duration is greater than 13.7 ms. Compared with the method of adding a polarizer to eliminate abnormal output horizontally polarized light, this method does not add elements in the laser, ensuring that the laser volume is relatively small, and does not affect the quality of the normal output laser. The research conclusion is thought to be of great practical significance, especially for processing transparent materials.

The added value of including citizen perspectives in a transition management process towards climate neutrality. Insights from an experience in the Swiss Alps

When citizens do not feel included in transition processes, implementation can become very difficult. Hence, we used a survey to include the perspective of citizens in one such transition process towards climate neutrality in the Swiss Alps, and analysed how this enhanced its legitimacy.Initiatives that foster transformative change often adopt a transdisciplinary approach by involving stakeholders from various sectors of society. Although transdisciplinary projects often emphasize a representative selection, it does not change the fact that a few stakeholders might develop visions, strategies, and policies that could affect many. A legitimate and transparent process is vital to ensure public acceptance and a successful implementation. In this study, we explore how citizens can be involved in transition management initiatives, based on a transition process towards climate neutrality in the Swiss Alps. Here, local citizens evaluated the vision developed by selected stakeholders, as well as the legitimacy of the process. The results were incorporated into workshops with the stakeholders. Based on the survey and interviews with stakeholders, we evaluate whether and how citizens’ views informed the transition process. Overall, citizens supported the vision developed by the stakeholders. The latter, in turn, highly valued the citizens’ perspectives, but were unsure about how to best integrate them. We conclude that the inclusion of citizens at an early stage can increase the legitimacy and transparency of transition processes.

Open Science in Agricultural Economics

Abstract We provide a ‘big picture’ of what Open Science is and what benefits, limitations, and risks it entails for agricultural economists. We show that Open Science comprises various aspects, such as, the accessibility of science, transparency of scientific processes, open and FAIR research data and code, and openness in teaching and education. We identify potential benefits of Open Science for individual researchers and the public, as well as adoption barriers. We highlight that public benefits of a wide-spread uptake of Open Science practices still remain unexplored. We share best practice examples for key aspects of agricultural economic research, i.e., primary data collection and analysis, optimization and simulation models, use of replication packages and an Open Science Community. Assessing the author guidelines of twelve Agricultural Economics journals for their Open Science practices, we find that data citation and transparency are considered important in many journals already, whereas replication, pre-registration or results-blind review are encouraged but rarely enforced. It also becomes evident that the journals differ in terms of how strictly they enforce their open-science guidelines. We close by providing recommendations for researchers, journal editors, policy makers, universities, research institutes and funding agencies to better align public benefits with private incentives.

STRATEGIES FOR USING SOCIAL PLATFORMS TO ATTRACT INVESTMENTS IN THE RECOVERY OF DEOCCUPIED TERRITORIES

The article addresses the complex issue of restoring Ukraine's de-occupied territories, which have suffered significant damage due to military actions. It emphasizes that the scale of destruction goes beyond ordinary material losses, encompassing both social and economic spheres, which significantly complicates the processes of restoration and reintegration of these territories. The recovery of regions left without proper infrastructure, damaged roads, bridges, and residential buildings requires a strategic approach that goes beyond physical rebuilding and includes long-term economic planning, social stabilization, and the integration of innovative solutions. The focus is on the role of economic revival as a key element of sustainable regional development. Given the limited state resources, attracting investments becomes a necessary tool for initiating economic processes, such as the restoration of production capacities, the creation of new enterprises, and the introduction of foreign technologies. The article also highlights the importance of the social aspect of recovery, including the reintegration of people who have lost their homes, jobs, or loved ones, which seriously affects their psychological well-being. The restoration of social infrastructure, education, and healthcare facilities is considered critical for ensuring basic living conditions and stability in the region. The work emphasizes the importance of implementing innovative solutions in the reconstruction process. Specifically, it explores the potential for developing "green" technologies and renewable energy, which could contribute to environmental sustainability and the region's energy independence. The authors propose the active use of digital technologies to increase the efficiency and transparency of recovery management processes, as well as to attract investments. Special attention is given to the use of social platforms as a tool for attracting investments and creating a positive image of the de-occupied territories on the international stage. The article analyzes the experience of other countries, such as the Balkan states, Colombia, and Iraq, which have used social media to promote their regions, attract investors, and enhance transparency. Based on this experience, the authors suggest creating special social media campaigns that may include webinars, online conferences, promotional videos, and infographics aimed at attracting investors' attention. The conclusions emphasize the need for a comprehensive approach to recovery, which includes economic, social, and technological aspects, as well as an effective communication strategy involving social platforms. Such an approach will ensure sustainable economic growth, social stability, and the integration of innovative technologies, which, in the long term, will contribute to the successful restoration of Ukraine’s de-occupied territories.

Capturing the health and well-being impact of climate change: a health impact assessment in Wales

Abstract Climate change is one of the biggest threats to global health in the 21st century and is likely to impact on health through a range of factors (societal, economic or environmental). The need to take action on climate and nature emergencies in order to protect population health and well-being and prevent negative impacts are becoming more urgent. Public Health Wales carried out a comprehensive mixed-method Health Impact Assessment (HIA) of climate change in Wales. HIA is a systematic process which considers the impact of a policy on the health and well-being of a population. It appraised the potential impacts of climate change on health and inequalities in Wales. This involved participatory workshops, engagement with stakeholders, statistical data, systematic literature reviews and case studies. The HIA findings indicate significant potential impacts across the determinants of health and mental well-being. For example, air quality, flooding, economic productivity, working conditions, access to services and community resilience. Impacts were identified across population groups, settings, and areas including urban and rural contexts, outdoor workers, children and young people, older people, schools, hospitals and workplaces. These findings have been beneficial to inform decision-makers to prepare for climate change plans and policies using a preventative evidence-informed approach. The work has demonstrated the value of HIA for significant, complex policies by mobilising a range of evidence through a transparent process, resulting in government and organizational action and transferrable learning for others. Key messages • HIA is a beneficial tool to identify at a granular level the health impacts of climate change. • The HIA has been used by the government of Wales and organisations to respond to and plan for climate change.

Trade as a commerical determinant of health: The health impact of a free trade agreement on Wales

Abstract As a result of the UK’s withdrawal from the European Union (‘Brexit’), the UK can negotiate its own trade agreements for the first time in over 40 years. This includes the Comprehensive and Progressive Trans-Pacific Partnership Agreement (CPTPP). Public Health Wales undertook a mixed-method Health Impact Assessment (HIA) to assess the potential impact of the CPTPP on health, well-being and inequalities in Wales. It is only the second ever HIA carried out on a free trade agreement (FTA) globally. A literature review on the potential impact of the CPTPP on health was conducted. Qualitative interviews with cross-sectoral representation, such as the environment, were undertaken alongside the development of a community health and demographic profile. The HIA identified significant potential impacts across health determinants, e.g., employment, agricultural production and health care, as well as impacts on population groups, e.g., those on low incomes. The Investor State Dispute Settlement (ISDS) mechanism, economic uncertainty and loss of regulatory alignment were identified as potential pathways for health impacts to affect the population. The HIA highlighted trade as a key commercial determinant of health. The findings have been beneficial in informing policy and decision-makers to prepare for the implementation of the CPTPP and take action to maximise opportunities and prevent potential negative impacts. Communicating HIA evidence that links trade to health has also led to valuable cross-sector and cross-UK engagement and sparked a “rethinking” of trade through a well-being economy lens where health, well-being and sustainability are primary objectives. This work has demonstrated the value of a HIA approach to FTAs by mobilising a range of evidence through a transparent process, resulting in transferrable learning for others as well providing an entry point for public health to have direct engagement with trade negotiators and those participating in trade discussions. Key messages • Trade agreements are a key commercial determinant of health across health behaviour, policy and economy. • Multiple population groups are affected by trade agreements both positively and negatively.

Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models

Brain tumors are among the most fatal and devastating diseases, and they often result in a significant reduction in life expectancy. The devising of treatment plans that can extend the lives of affected individuals hinges on an accurate diagnosis of these tumors. Identifying and analyzing large volumes of magnetic resonance imaging (MRI) data manually proves to be both challenging and time-consuming. As a result, there exists a pressing need for a reliable machine-learning approach to accurately diagnose brain tumors, and numerous methods have already been proposed over the last decade. In this paper, a novel, comprehensive approach is proposed for identifying and classifying a given MR brain image as abnormal. Three common brain diseases, namely glioma, meningioma, and pituitary tumor, are chosen as abnormal brains, and the Figshare MRI brain image dataset was collected from the Kaggle and IEEE websites. The proposed method is initiated by employing 1st-order statistics, 2nd-order statistics, and higher-order transformed (DWT) feature extraction to extract features from images. Then missing data is addressed and handled using KNNImputer, followed by the application of the ExtratreesClassifier and PCA feature selection methods to identify the most relevant features and reduce the dimensions of these features. Subsequently, the reduced features are submitted to seven machine learning models, namely RF, GB, CB, SVM, LGBM, DT, and LR. The strategy of k-fold cross-validation is utilized to enhance the performance of those models. Finally, the models are evaluated using XAI approaches, which ensure transparent decision-making processes and provide insights into the model’s predictions. Remarkably, our approach achieves the highest accuracy, precision, recall, F1 score, MCC, Kappa, AUC-ROC, and R2, as well as the lowest loss, among the seven models evaluated, proving its effectiveness and applicability in multiple analytic applications relying on publicly available datasets.

Clinical recommendations for polygenic risk score-enhanced breast cancer screening: Can.Heal project

Abstract Background The application of polygenic risk score (PRS) in breast cancer (BC) screening presents promising opportunities. Developing recommendations for future use and research on this topic is a key focus of the EU4Health project: Building the EU cancer and public health genomics platform (Can.Heal). We aim to provide these recommendations based on the analysis of available evidence through a transparent and rigorous development process. Methods The recommendations adopt the GRADE evidence to decision methodology, leveraging an evidence review team and a multidisciplinary panel of nine experts. A systematic review is being conducted to evaluate the evidence for PRS in BC screening, in the domains of benefits and harms, acceptability, feasibility, equity and cost-effectiveness. Results Regarding benefits and harms of adding PRS to BC standard screening, we identified 63 relevant articles. Forty-five (71%) discussed benefits, while 14 (22%) addressed harms. Forty-two (67%) were observational studies, 18 (28%) modeling studies and 1 non-randomised control trial (2%) that examined the diagnostic accuracy of PRS-enhanced screening for relative BC risk prediction using measures such as net reclassification index and area under the curve. Lastly, two modeling studies (3%) assessed the clinical utility of PRS-enhanced screening in terms of life years gained, BC deaths averted. The other domains are under examination. The panel convened to redefine the research question and outcomes of interest and will reconvene to assess the certainty of evidence collected, and subsequently to draft recommendations. Conclusions The integration of PRS into BC screening shows potential benefits in improving risk prediction. Ongoing trials, such as Wisdom and MyPEBS, are studying the clinical utility of integrating PRS in BC screening. The approach taken by the Can.Heal aims to ensure that the recommendations are based on a thorough and balanced evaluation of the available evidence. Key messages • The Can.Heal project aims to formulate policy and research recommendations for breast cancer screening using PRS through a thorough evidence review. • Upcoming PRS recommendations rely on expert opinion and modeling studies.