Consensus Method Research Articles

Carbon monitoring, reporting and verification (MRV) for cleaner built environment: Developing a solar photovoltaic blockchain tool and applications in Hong Kong's building sector

Photovoltaic (PV) systems offer significant potential for net-zero emission targets, due to their flexibility and high exploitation capacity. However, effective carbon monitoring, reporting, and verification (MRV) methods are crucial for their life cycle. The present study introduces a novel blockchain-based carbon auditing tool aimed at supporting the life cycle assessment of PV systems in Hong Kong's building sector. Consequently, all relevant information throughout the PV system's life cycle is uploaded and securely recorded within the blockchain system. The decentralized network structure, distributed consensus, and cryptographic methods employed by blockchain technology ensure the traceability, immutability, and transparency of the recorded information. Furthermore, this study provides an analysis of the installation potential and life cycle environmental benefits of PV systems in Hong Kong's building sector. Finally, the feasibility of the proposed framework is demonstrated through case studies conducted in Hong Kong. These findings underline the reliability of the proposed blockchain-based tool as a technological foundation for carbon MRV in the Hong Kong building PV sector.

Creating a Culturally Safe Online Data Collection Instrument to Measure Vaccine Confidence Among Indigenous Youth: Indigenous Consensus Method.

Participating in surveys can shape the perception of participants related to the study topic. Administering a vaccine hesitancy questionnaire can have negative impacts on participants' vaccine confidence. This is particularly true for online and cross-cultural data collection because culturally safe health education to correct misinformation is typically not provided after the administration of an electronic survey. To create a culturally safe, online, COVID-19 vaccine confidence survey for Indigenous youth designed to collect authentic, culturally relevant data of their vaccine experiences, with a low risk of contributing to further vaccine confusion among participants. Using the Aboriginal Telehealth Knowledge Circle consensus method, a team of academics, health care providers, policy makers, and community partners reviewed COVID-19 vaccine hesitancy surveys used in public health research, analyzed potential risks, and created a framework for electronic Indigenous vaccine confidence surveys as well as survey items. The framework for safer online survey items is based on 2 principles, a first do-no-harm approach and applying a strengths-based lens. Relevant survey domains identified in the process include sociodemographic information, participants' connection to their community, preferred sources for health information, vaccination uptake among family members and peers, as well as personal attitudes toward vaccines. A total of 44 survey items were developed, including 5 open-ended items to improve the authenticity of the data and the analysis of the experiences of Indigenous youth. Using an Indigenous consensus method, we have developed an online COVID-19 vaccine confidence survey with culturally relevant domains and reduced the risk of amplifying misinformation and negative impacts on vaccine confidence among Indigenous participants. Our approach can be adapted to other online survey development in collaboration with Indigenous communities.

Proof-of-Stake Non-Fungible Tokens, the Distributed Autonomous Organization, and the Valuation of Art: A Proposal for a Nonprofit, Community Controlled NFT

Abstract This paper describes a way to use blockchain-based non-fungible tokens (NFTs) to address some fundamental failures of the art world introduced when the early-twentieth-century evolution of financial markets expanded to include objets d’art as commodities. It proposes the creation of a distributed autonomous organization (DAO) that uses domain experts’ Delphic consensus methods embedded in smart contracts to provide NFTs with meaningful nonfinancial assessments of intrinsic artistic qualities and merit. Full details of the blockchain implementation and organizational development are online, registered at https://doi.org/10.17605/OSF.IO/3DF4W with the Open Science Foundation as a large-scale participant/observer social science experiment.

Distributed Optimization of Graph Convolutional Network Using Subgraph Variance.

In recent years, distributed graph convolutional networks (GCNs) training frameworks have achieved great success in learning the representation of graph-structured data with large sizes. However, existing distributed GCN training frameworks require enormous communication costs since a multitude of dependent graph data need to be transmitted from other processors. To address this issue, we propose a graph augmentation-based distributed GCN framework (GAD). In particular, GAD has two main components: GAD-Partition and GAD-Optimizer. We first propose an augmentation-based graph partition (GAD-Partition) that can divide the input graph into augmented subgraphs to reduce communication by selecting and storing as few significant vertices of other processors as possible. To further speed up distributed GCN training and improve the quality of the training result, we design a subgraph variance-based importance calculation formula and propose a novel weighted global consensus method, collectively referred to as GAD-Optimizer. This optimizer adaptively adjusts the importance of subgraphs to reduce the effect of extra variance introduced by GAD-Partition on distributed GCN training. Extensive experiments on four large-scale real-world datasets demonstrate that our framework significantly reduces the communication overhead ( ≈ 50% ), improves the convergence speed ( ≈ 2 × ) of distributed GCN training, and obtains a slight gain in accuracy ( ≈ 0.45% ) based on minimal redundancy compared to the state-of-the-art methods.

The rootstock modifies the arbuscular mycorrhizal community of the root system, while the influence of the scion is limited in grapevines.

Understanding the effects of grapevine rootstock and scion genotypes on arbuscular mycorrhizal fungi (AMF), as well as the roles of these fungi in plant development, could provide new avenues for adapting viticulture to climate change and reducing agrochemical inputs. The root colonization of 10 rootstock/scion combinations was studied using microscopy and metabarcoding approaches and linked to plant development phenotypes. The AMF communities were analysed using 18S rRNA gene sequencing. The 28S rRNA gene was also sequenced for some combinations to evaluate whether the method changed the results. Root colonization indexes measured by microscopy were not significantly different between genotypes. Metabarcoding analyses showed an effect of the rootstock genotype on the β-diversity and the enrichment of several taxa with both target genes, as well as an effect on the Chao1 index with the 18S rRNA gene. We confirm that rootstocks recruit different AMF communities when subjected to the same pedoclimatic conditions, while the scion has little or no effect. Significant correlations were observed between AMF community composition and grapevine development, suggesting that AMF have a positive effect on plant growth. Given these results, it will be important to define consensus methods for studying the role of these beneficial micro-organisms in vineyards.

Prescribing Appropriate Medicines to Older Adults: A Finnish Experience with the Web-Based Meds75+ Database.

The Finnish web-based Meds75+ database supports rational, safe and appropriate prescribing to older adults in primary care. This article describes the content and updating process of Meds75+ and demonstrates its applicability in everyday clinical practice. Meds75+ contains a classification (A-D) and recommendation texts for 450-500 drug substances when used in the treatment of older adults aged 75 years or older. The content of Meds75+ is continually updated. Each assessment of a drug substance begins with a structured collection of available information and research evidence. After that, an interdisciplinary expert panel discusses the classification and recommendation using a consensus method. A rolling 3-year updating cycle guarantees that all drug substances are reviewed regularly. Most drug substances are classified as class A (41%) (suitable, e.g. bisoprolol) or as class C (37%) (suitable with specific precautions, e.g. ibuprofen). One-fifth (20%) of the substances are in class D (avoid use, e.g. diazepam). Most commonly, older adults have purchased substances affecting the alimentary tract and metabolism (17%), the nervous system (16%) and the cardiovascular system (15%). In Finland, the proportion of older adults using class D substances (37%) has not changed between the years 2019 and 2022. Meds75+ has potential to support safer and more effective use of medications for older adults, since it offers up-to-date information on drug substances for healthcare professionals.

A Novel Hierarchical High-Dimensional Unsupervised Active Learning Method

This paper processes a novel hierarchical high-dimensional clustering algorithm based on the Active Learning Method (ALM), which is a fuzzy-learning algorithm. The hierarchical part of the algorithm is composed of two phases: divisible and agglomerative. The divisible phase, a zooming-in-process, searches for sub-clusters in already-found clusters hierarchically. At each level of the hierarchy, the clusters are found by an ensemble clustering method based on the density of data. This part of the algorithm blurs each data point as multiple one-dimensional fuzzy membership functions called ink-drop patterns; then, it accumulates the ink-drop patterns of all data points on every dimension separately. Next, it performs one-dimensional density partitioning to produce an ensemble of clustering solutions; after that, combining the results is done based on a novel consensus method with the aid of prime numbers. An agglomerative phase is a bottom-up approach that merges clusters based on a novel distance metric, named K2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${K}^{2}$$\\end{document}-nearest neighbor. The algorithm is named as the Hierarchical High-Dimensional Unsupervised Active Learning Method (HiDUALM) and is explained in more detail throughout this paper. Although the classical clustering methods are not suitable for high-dimensional data clustering, the proposed method solves the problems related to speed and memory using ensemble learning, while due to its hierarchy and the use of different distance criteria, different levels of the cluster provide the clause. Experiments on synthetic and real-world datasets are presented to show the effectiveness of the proposed-clustering algorithm.

CSSP-2.0: A refined consensus method for accurate protein secondary structure prediction

Studying the relationship between sequences and their corresponding three-dimensional structure assists structural biologists in solving the protein-folding problem. Despite several experimental and in-silico approaches, still understanding or decoding the three-dimensional structures from the sequence remains a mystery. In such cases, the accuracy of the structure prediction plays an indispensable role. To address this issue, an updated web server (CSSP-2.0) has been created to improve the accuracy of our previous version of CSSP by deploying the existing algorithms. It uses input as probabilities and predicts the consensus for the secondary structure as a highly accurate three-state Q3 (helix, strand, and coil). This prediction is achieved using six recent top-performing methods: MUFOLD-SS, RaptorX, PSSpred v4, PSIPRED, JPred v4, and Porter 5.0. CSSP-2.0 validation includes datasets involving various protein classes from the PDB, CullPDB, and AlphaFold databases. Our results indicate a significant improvement in the accuracy of the consensus Q3 prediction. Using CSSP-2.0, crystallographers can sort out the stable regular secondary structures from the entire complex structure, which would aid in inferring the functional annotation of hypothetical proteins. The web server is freely available at https://bioserver3.physics.iisc.ac.in/cgi-bin/cssp-2/

Development of mental health first aid guidelines for psychosis for Brazil: A Delphi expert consensus study.

Psychotic symptoms can be highly debilitating for those experiencing them. Community members, including family and friends, can play a crucial role in providing support to a person during the early stages of psychosis, provided they have the necessary resources. Mental health first aid guidelines for psychosis have been developed for high-income countries and this study aimed to adapt those guidelines for Brazil. A Delphi expert consensus method was used to gather the views and opinions of 28 health professionals and 24 individuals with lived experience of psychosis in Brazil over two survey rounds. Firstly, 403 statements were translated from English to Brazilian-Portuguese. In the Round 1 survey, participants were asked to rate each statement based on how important they believed it was for it to be included in the Brazilian guidelines. They were also asked to suggest new actions if they wished. Consensus was reached on 257 statements. Eight new statements were created and endorsed from panelists' comments, and a further 45 statements were unique to the Brazilian guidelines. There was a modest level of similarity between the English-language and Brazilian guidelines. However, the Brazilian guidelines had a greater focus on the importance of family support for people with psychosis and stigma as a possible barrier for openly discussing help-seeking actions for mental health problems in Brazil.

Commentary: core descriptor sets using consensus methods support ‘table one’ consistency

BackgroundInconsistent reporting of patient characteristics in clinical research hampers reproducibility and limits analysis opportunities. This paper proposes condition-specific ‘Core Descriptor Sets’ comprising key factors like demographics, disease severity, comorbidities, and prognosis to standardize Table 1 reporting. MethodsDevelopment entails stakeholder involvement, systematic identification of descriptors, value rating, and consensus-building using multiple Delphi rounds. Final agreement comes at an expert meeting. ConclusionBenefits include easier cross-study comparison, for example, through individual patient meta-analysis, facilitated by comparison of consistently reported individual data rather than group-level analysis. This may also support routine data analyses, subgroup and risk identification, and reduced research waste. Core Descriptor Sets describe cohorts thoroughly while minimizing research burden. They are intended to enable improved clinical characterization, personalization, reproducibility, data sharing, and knowledge building.

Identification of distinct genotypes in circulating RSV A strains based on variants in the virus replication-associated genes.

Respiratory syncytial virus (RSV) is a common cause of respiratory infection that often leads to hospitalization of infected younger children and older adults. RSV is classified into two strains, A and B, each with several subgroups or genotypes. One issue with the definition of these subgroups is the lack of a unified method of identification or genotyping. We propose that genotyping strategies based on the genes coding for replication-associated proteins could provide critical information on the replication capacity of the distinct subgroups, while clearly distinguishing genotypes. Here, we analyzed the virus replication-associated genes N, P, M2, and L from de novo assembled RSV A sequences obtained from 31 newly sequenced samples from hospitalized patients in Philadelphia and 78 additional publicly available sequences from different geographic locations within the United States. In-depth analysis and annotation of variants in the replication-associated proteins identified the polymerase protein L as a robust target for genotyping RSV subgroups. Importantly, our analysis revealed non-synonymous variations in L that were consistently accompanied by conserved changes in its co-factor P or the M2-2 protein, suggesting associations and interactions between specific domains of these proteins. Similar associations were seen among sequences of the related human metapneumovirus. These results highlight L as an alternative to other RSV genotyping targets and demonstrate the value of in-depth analyses and annotations of RSV sequences as it can serve as a foundation for subsequent in vitro and clinical studies on the efficiency of the polymerase and fitness of different virus isolates.IMPORTANCEGiven the historical heterogeneity of respiratory syncytial virus (RSV) and the disease it causes, there is a need to understand the properties of the circulating RSV strains each season. This information would benefit from an informative and consensus method of genotyping the virus. Here, we carried out a variant analysis that shows a pattern of specific variations among the replication-associated genes of RSV A across different seasons. Interestingly, these variation patterns, which were also seen in human metapneumovirus sequences, point to previously defined interactions of domains within these genes, suggesting co-variation in the replication-associated genes. Our results also suggest a genotyping strategy that can prove to be particularly important in understanding the genotype-phenotype correlation in the era of RSV vaccination, where selective pressure on the virus to evolve is anticipated. More importantly, the categorization of pneumoviruses based on these patterns may be of prognostic value.

Visible fluid motion on manipulation as the new threshold for intraoperatively determined knee arthroplasty component loosening: ADelphi study.

There is a lack of a clear, uniform definition for intraoperatively assessed component loosening of a knee arthroplasty component, complicating the interpretation and interchangeability of results of diagnostic studies using an intraoperative observation as the reference test. The purpose of this study was to establish a consensus among specialised knee revision surgeons regarding the definition of intraoperatively determined loosening of total or unicondylar knee arthroplasty components. Utilising the Delphi consensus method, an international panel of highly specialised knee revision surgeons was invited to participate in a three-round process. The initiation of the first round involved the exploration of possible criteria for intraoperatively determined loosening with open questions. The second round focused on rating these criteria importance on a five-point Likert scale. For the third round, criteria that reached consensus were summarised in consecutive definitions for intraoperatively determined loosening and proposed to the panel. Consensus was established when over 70% of participants agreed with a definition for intraoperatively determined loosening. The 34 responding panel members described in total 60 different criteria in the first round of which 34 criteria received consensus in the second round. Summarising these criteria resulted in four different definitions as minimal requirements for intraoperatively determined loosening. Eighty-eight percent of the panel members agreed on defining a component as loose if there is visible fluid motion at the interface observed during specific movements or when gently applying direct force. This study successfully established a consensus using a Delphi method among knee revision surgeons on the definition of intraoperatively determined component loosening. By agreeing on the visibility of fluid motion as new definition, this study provides a standardised reference for future diagnostic research. This definition will enhance the interpretability and interchangeability of future diagnostic studies evaluating knee arthroplasty component loosening. Level V.

Towards a Consensus Method for the Isolation of Microplastics from Freshwater Sediments

Towards a Consensus Method for the Isolation of Microplastics from Freshwater Sediments

Tropical convexity in location problems

We investigate location problems where the optimal solution is found within the tropical convex hull of the given input points. Our initial focus is on geodesically star-convex sets, using the asymmetric tropical distance. We introduce the concept of tropically quasiconvex functions, which have sub-level sets with this shape, and are closely related to monotonic functions. Our findings demonstrate that location problems using tropically quasiconvex functions as distance measures will result in an optimal solution within the tropical convex hull of the input points. We also extend this result to cases where the input points are replaced with tropically convex sets. Finally, we explore the applications of our research in phylogenetics, highlighting the properties of consensus methods that arise from our class of location problems.

Development and validation of measures of pharmacists’ acceptability and willingness to screen for perinatal depression

BackgroundWhile pharmacists' roles in mental healthcare are expanding, research exploring pharmacists' acceptability and willingness to provide mental health services is limited. This study developed and validated theory-driven measures of pharmacists’ acceptability and willingness to screen for perinatal depression in community pharmacy settings. Materials/MethodsItems were developed using published literature and the Theoretical Framework of Acceptability (TFA), then content validated using consensus methods with experts who completed the content validity index (CVI). The revised items were disseminated to pharmacists in Australia. Responses were analysed descriptively. Exploratory factor analyses (EFA) were used to explore the factorial structure and generate scales. Multivariate regression analysis was conducted to explore predictors of willingness. ResultsA 58-item questionnaire was developed, encompassing the 7 domains of the TFA and an eighth domain (willingness). The average CVI was 0.92, domain range (0.88–0.96). The universal CVI was 56/58. Expert feedback informed item revision, creation and deletion. Pharmacists' responses (n = 157) to the final 42-item questionnaire indicated overall acceptance and willingness to conduct PND screening. However, perceived knowledge was lacking. The EFA resulted a two-factor solution (1 = acceptance; 2 = self-efficacy). The measurement scales created had good internal consistency. In multivariate regression analysis, ‘Acceptance’ (Beta = 0.949 (0.760–1.103)) and ‘Self-Efficacy’ (Beta = 0.107 (0.036–0.174)) were significant predictors of ‘Willingness’ and the model predicted 77 % of the variation in ‘Willingness’. ConclusionsPsychometrically-sound measures of pharmacists’ acceptability and willingness to screen for PND have been developed with stakeholder input. The questionnaire can be used for standardised measurement of these constructs across studies.

A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations.

Environmental chemicals, such as PFAS, exist as mixtures and are frequently encountered at varying concentrations, which can lead to serious health effects, such as cancer. Therefore, understanding the dose-dependent toxicity of chemical mixtures is essential for health risk assessment. However, comprehensive methods to assess toxicity and identify the mechanisms of these harmful mixtures are currently absent. In this study, the dose-dependent toxicity assessments of chemical mixtures are performed in three methodologically distinct phases. In the first phase, we evaluated our machine-learning method (AI-HNN) and pathophysiology method (CPTM) for predicting toxicity. In the second phase, we integrated AI-HNN and CPTM to establish a comprehensive new approach method (NAM) framework called AI-CPTM that is targeted at refining prediction accuracy and providing a comprehensive understanding of toxicity mechanisms. The third phase involved experimental validations of the AI-CPTM predictions. Initially, we developed binary, multiclass classification, and regression models to predict binary, categorical toxicity, and toxic potencies using nearly a thousand experimental mixtures. This empirical dataset was expanded with assumption-based virtual mixtures, compensating for the lack of experimental data and broadening the scope of the dataset. For comparison, we also developed machine-learning models based on RF, Bagging, AdaBoost, SVR, GB, KR, DT, KN, and Consensus methods. The AI-HNN achieved overall accuracies of over 80%, with the AUC exceeding 90%. In the final phase, we demonstrated the superior performance and predictive capability of AI-CPTM, including for PFAS mixtures and their interaction effects, through rigorous literature and statistical validations, along with experimental dose-response zebrafish-embryo toxicity assays. Overall, the AI-CPTM approach significantly improves upon the limitations of standalone AI models, showing extensive enhancements in identifying toxic chemicals and mixtures and their mechanisms. This study is the first to develop a hybrid NAM that integrates AI with a pathophysiology method to comprehensively predict chemical-mixture toxicity, carcinogenicity, and mechanisms.

An Improved Laplacian Gravity Centrality-Based Consensus Method for Social Network Group Decision-Making with Incomplete ELICIT Information

With the advancement of information technology, social media has become increasingly prevalent. The complex networks of social relationships among decision-makers (DMs) have given rise to the problem of social network group decision-making (SNGDM), which has garnered considerable attention in recent years. However, most existing consensus-reaching methods in SNGDM only consider local network information when determining the influence of DMs within the social network. This approach fails to adequately reflect the crucial role of key DMs in regulating information propagation during the consensus-reaching process. Additionally, the partial absence of linguistic evaluations in the decision-making problems also poses obstacles to identifying the optimal alternative. Therefore, this paper proposes an improved Laplacian gravity centrality-based consensus method that can effectively handle incomplete decision information in social network environments. First, the extended comparative linguistic expressions with symbolic translation (ELICIT) are utilized to describe DMs’ linguistic evaluations and construct the incomplete decision matrix. Second, the improved Laplacian gravity centrality (ILGC) is proposed to quantify the influence of DMs in the social network by considering local and global topological structures. Based on the ILGC measure, we develop a trust-driven consensus-reaching model to enhance group consensus, which can better simulate opinion interactions in real-world situations. Lastly, we apply the proposed method to a smart city evaluation problem. The results show that our method can more reasonably handle incomplete linguistic evaluations, more comprehensively capture the influence of DMs, and more effectively improve group consensus.

Expert Consensus Methods In The Humanities - An Exploration of their Potential

Background Expert consensus methods are regularly used in natural, social, and life sciences. This article explores the potential of applying these methods more frequently in humanities research. Methods The authors reviewed literature and applied the philosophical methods of conceptual analysis and conceptual engineering. Results This article identifies and describes six main elements of expert consensus methods. It also provides an overview of the different types of expert consensus methods regularly used in the natural, social, and life sciences: Delphi studies, nominal groups, consensus conferences, and Glaser’s state of the art method. Subsequently, each of these types is illustrated by an example from the sciences. The article also presents the potential of and objections to the application of expert consensus methods there. It gives four examples of expert consensus methods that were applied in humanities research, also presented in line with the six elements. Conclusions The comparisons and categorization show that, as in the natural, social, and life sciences, expert consensus methods in the humanities can in some instances potentially accelerate the epistemic process and enhance transparency, replicability, diversity, and fair processes. Nevertheless, expert consensus methods need to be fine-tuned to do justice to the unique nature and approaches of the humanities and therefore further research is needed.

Uncertainty in consensus predictions of plant species' vulnerability to climate change

AbstractAimVariation in spatial predictions of species' ranges made by various models has been recognized as a significant source of uncertainty for modelling species distributions. Consensus approaches that combine the results of multiple models have been employed to reduce the uncertainty introduced by different algorithms. We evaluate how estimates of habitat suitability, projected using species distribution models (SDMs), varied among different consensus methods relative to the variation introduced by different global climate models (GCMs) and representative concentration pathways (RCPs) used for projection.LocationCalifornia Floristic Province (California, US portion).MethodsWe modelled the current and future potential distributions of 82 terrestrial plant species, developing model predictions under different combinations of GCMs, RCPs, time periods, dispersal assumptions and SDM consensus methods commonly used to combine different species distribution modelling algorithms. We assessed how each of these factors contributed to the variability in future predictions of species habitat suitability change and aggregate measures of proportional change in species richness. We also related variability in species‐level habitat change to species' attributes.ResultsAssuming full dispersal capacity, the variability between habitat predictions made by different consensus methods was higher than the variability introduced by different RCPs and GCMs. The relationships between species' attributes and variability in future habitat predictions depended on the source of uncertainty and dispersal assumptions. However, species with small ranges or low prevalence tended to be associated with high variability in range change forecasts.Main ConclusionsOur results support exploring multiple consensus approaches when considering changes in habitat suitability outside of species' current distributions, especially when projecting species with low prevalence and small range sizes, as these species tend to be of the greatest conservation concern yet produce highly variable model outputs. Differences in vulnerability between diverging greenhouse gas concentration scenarios are most readily observed for end‐of‐century time periods and within species' currently occupied habitats (no dispersal).

Identifying research priorities for improving information and support for patients undergoing breast cancer surgery: a UK patient-centred priority setting project

PurposeTo use robust consensus methods with individuals with lived breast cancer experience to agree the top 10 research priorities to improve information and support for patients undergoing breast cancer surgery in the UK.MethodsResearch uncertainties related to information and support for breast cancer surgery submitted by patients and carers were analysed thematically to generate summary questions for inclusion in an online Delphi survey. Individuals with lived breast cancer experience completed two Delphi rounds including feedback in which they selected their top 10 research priorities from the list provided. The most highly ranked priorities from the survey were discussed at an in-person prioritisation workshop at which the final top 10 was agreed.ResultsThe 543 uncertainties submitted by 156 patients/carers were categorised into 63 summary questions for inclusion in the Delphi survey. Of the 237 individuals completing Round 1, 190 (80.2%) participated in Round 2. The top 25 survey questions were carried forward for discussion at the in-person prioritisation workshop at which 17 participants from across the UK agreed the final top 10 research priorities. Key themes included ensuring patients were fully informed about all treatment options and given balanced, tailored information to support informed decision-making and empower their recovery. Equity of access to treatments including contralateral mastectomy for symmetry was also considered a research priority.ConclusionThis process has identified the top 10 research priorities to improve information and support for patients undergoing breast cancer surgery. Work is now needed to develop studies to address these important questions.