Universal Principled Review: A Community-Driven Method to Improve Peer Review.

Despite being a staple of our science, the process of pre-publication peer review has few agreed-upon standards defining its goals or ideal execution. As a community of reviewers and authors, we assembled an evaluation format and associated specific standards for the process as we think it should be practiced. We propose that we apply, debate, and ultimately extend these to improve the transparency of our criticism and the speed with which quality data and ideas become public.

Guidelines and definitions for research on epithelial-mesenchymal transition.

Epithelial-mesenchymal transition (EMT) encompasses dynamic changes in cellular organization from epithelial to mesenchymal phenotypes, which leads to functional changes in cell migration and invasion. EMT occurs in a diverse range of physiological and pathological conditions and is driven by a conserved set of inducing signals, transcriptional regulators and downstream effectors. With over 5,700 publications indexed by Web of Science in 2019 alone, research on EMT is expanding rapidly. This growing interest warrants the need for a consensus among researchers when referring to and undertaking research on EMT. This Consensus Statement, mediated by 'the EMT International Association' (TEMTIA), is the outcome of a 2-year-long discussion among EMT researchers and aims to both clarify the nomenclature and provide definitions and guidelines for EMT research in future publications. We trust that these guidelines will help to reduce misunderstanding and misinterpretation of research data generated in various experimental models and to promote cross-disciplinary collaboration to identify and address key open questions in this research field. While recognizing the importance of maintaining diversity in experimental approaches and conceptual frameworks, we emphasize that lasting contributions of EMT research to increasing our understanding of developmental processes and combatting cancer and other diseases depend on the adoption of a unified terminology to describe EMT.

Best practices for data management and sharing in experimental biomedical research.

Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable validation and building on results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers' careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. This review provides critical insights into raw and processed data, metadata, high-throughput versus low-throughput datasets, a common language for documentation, experimental and reporting guidelines, efficient data management systems, sharing practices, and relevant repositories. We systematically present available resources and optimal practices for wide use by experimental biomedical researchers.

Advancing the microbiome research community.

The human microbiome has become a recognized factor in promoting and maintaining health. We outline opportunities in interdisciplinary research, analytical rigor, standardization, and policy development for this relatively new and rapidly developing field. Advances in these aspects of the research community may in turn advance our understanding of human microbiome biology.

Virology-the path forward.

In the United States (US), biosafety and biosecurity oversight of research on viruses is being reappraised. Safety in virology research is paramount and oversight frameworks should be reviewed periodically. Changes should be made with care, however, to avoid impeding science that is essential for rapidly reducing and responding to pandemic threats as well as addressing more common challenges caused by infectious diseases. Decades of research uniquely positioned the US to be able to respond to the COVID-19 crisis with astounding speed, delivering life-saving vaccines within a year of identifying the virus. We should embolden and empower this strength, which is a vital part of protecting the health, economy, and security of US citizens. Herein, we offer our perspectives on priorities for revised rules governing virology research in the US.

Clinical research in endometrial cancer: consensus recommendations from the Gynecologic Cancer InterGroup.

The Gynecologic Cancer InterGroup (GCIG) Endometrial Cancer Consensus Conference on Clinical Research (ECCC) was held in Incheon, South Korea, Nov 2-3, 2023. The aims were to develop consensus statements for future trials in endometrial cancer to achieve harmonisation on design elements, select important questions, and identify unmet needs. All 33 GCIG member groups participated in the development, refinement, and finalisation of 18 statements within four topic groups, addressing adjuvant treatment in high-risk disease; treatment for metastatic and recurrent disease; trial designs for rare endometrial cancer subgroups and special circumstances; and specific methodology and adaptation for trials in low-resource settings. In addition, eight areas of unmet need were identified. This was the first GCIG Consensus Conference to include patient advocates and an expert on inclusion, diversity, equity, and access to take part in all aspects of the process and output. Four early-career investigators were also selected for participation, ensuring that they represented different GCIG member groups and regions. Unanimous consensus was obtained for 16 of the 18 statements, with 97% concordance for the remaining two. Using the described methodology from previous Ovarian Cancer Consensus Conferences, this conference did not require even one minority statement. The high acceptance rate following active involvement in the preparation, discussion, and refinement of the statements by all representatives confirmed the consensus progress within a global academic setting, and the expectation that the ECCC will lead to greater harmonisation, actualisation, inclusion, and resolution of unmet needs in clinical research for individuals living with and beyond endometrial cancer worldwide.

ACcurate COnsensus Reporting Document (ACCORD) explanation and elaboration: Guidance and examples to support reporting consensus methods.

BACKGROUND: When research evidence is limited, inconsistent, or absent, healthcare decisions and policies need to be based on consensus amongst interested stakeholders. In these processes, the knowledge, experience, and expertise of health professionals, researchers, policymakers, and the public are systematically collected and synthesised to reach agreed clinical recommendations and/or priorities. However, despite the influence of consensus exercises, the methods used to achieve agreement are often poorly reported. The ACCORD (ACcurate COnsensus Reporting Document) guideline was developed to help report any consensus methods used in biomedical research, regardless of the health field, techniques used, or application. This explanatory document facilitates the use of the ACCORD checklist. METHODS AND FINDINGS: This paper was built collaboratively based on classic and contemporary literature on consensus methods and publications reporting their use. For each ACCORD checklist item, this explanation and elaboration document unpacks the pieces of information that should be reported and provides a rationale on why it is essential to describe them in detail. Furthermore, this document offers a glossary of terms used in consensus exercises to clarify the meaning of common terms used across consensus methods, to promote uniformity, and to support understanding for consumers who read consensus statements, position statements, or clinical practice guidelines. The items are followed by examples of reporting items from the ACCORD guideline, in text, tables and figures. CONCLUSIONS: The ACCORD materials - including the reporting guideline and this explanation and elaboration document - can be used by anyone reporting a consensus exercise used in the context of health research. As a reporting guideline, ACCORD helps researchers to be transparent about the materials, resources (both human and financial), and procedures used in their investigations so readers can judge the trustworthiness and applicability of their results/recommendations.

Guidelines for mechanistic modeling and analysis in cardiovascular research.

Computational, or in silico, models are an effective, noninvasive tool for investigating cardiovascular function. These models can be used in the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. Recent advances in computing power and data management have led to innovative and complex modeling frameworks that simulate cardiovascular function across multiple scales. While commonly used in multiple disciplines, there is a lack of concise guidelines for the implementation of computer models in cardiovascular research. In line with recent calls for more reproducible research, it is imperative that scientists adhere to credible practices when developing and applying computational models to their research. The goal of this manuscript is to provide a consensus document that identifies best practices for in silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. We outline rigorous practices for computational, mechanistic modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data.

Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers.

The rapid growth of high-throughput technologies has transformed biomedical research. With the increasing amount and complexity of data, scalability and reproducibility have become essential not just for experiments, but also for computational analysis. However, transforming data into information involves running a large number of tools, optimizing parameters, and integrating dynamically changing reference data. Workflow managers were developed in response to such challenges. They simplify pipeline development, optimize resource usage, handle software installation and versions, and run on different compute platforms, enabling workflow portability and sharing. In this Perspective, we highlight key features of workflow managers, compare commonly used approaches for bioinformatics workflows, and provide a guide for computational and noncomputational users. We outline community-curated pipeline initiatives that enable novice and experienced users to perform complex, best-practice analyses without having to manually assemble workflows. In sum, we illustrate how workflow managers contribute to making computational analysis in biomedical research shareable, scalable, and reproducible.

Best practices and tools for reporting reproducible fluorescence microscopy methods.

Although fluorescence microscopy is ubiquitous in biomedical research, microscopy methods reporting is inconsistent and perhaps undervalued. We emphasize the importance of appropriate microscopy methods reporting and seek to educate researchers about how microscopy metadata impact data interpretation. We provide comprehensive guidelines and resources to enable accurate reporting for the most common fluorescence light microscopy modalities. We aim to improve microscopy reporting, thus improving the quality, rigor and reproducibility of image-based science.

IOF position on scientists and societies operating in conflict zones.

This position paper of the International Osteoporosis Foundation reports the findings of an IOF Commission to consider to recommend rules of partnership with scientists belonging to a country which is currently responsible for an armed conflict, anywhere in the world. The findings and recommendations have been adopted unanimously by the Board of IOF.

Operationalizing Equity, Inclusion, and Access in Research Practice at a Large Academic Institution.

INTRODUCTION: Healthcare advances are hindered by underrepresentation in prospective research; sociodemographic, data, and measurement infidelity in retrospective research; and a paucity of guidelines surrounding equitable research practices. OBJECTIVE: The Joint Research Practices Working Group was created in 2021 to develop and disseminate guidelines for the conduct of inclusive and equitable research. METHODS: Volunteer faculty and staff from two research centers at the University of Pennsylvania initiated a multi-pronged approach to guideline development, including literature searches, center-level feedback, and mutual learning with local experts. RESULTS: We developed guidelines for (1) participant payment and incentives; (2) language interpretation and translation; (3) plain language in research communications; (4) readability of study materials; and (5) inclusive language for scientific communications. Key recommendations include (1) offer cash payments and multiple payment options to participants when required actions are completed; (2) identify top languages of your target population, map points of contact, and determine available interpretation and translation resources; (3) assess reading levels of materials and simplify language, targeting 6th- to 8th-grade reading levels; (4) improve readability through text formatting and style, symbols, and visuals; and (5) use specific, humanizing terms as adjectives rather than nouns. CONCLUSIONS: Diversity, inclusion, and access are critical values for research conduct that promotes justice and equity. These values can be operationalized through organizational commitment that combines bottom-up and top-down approaches and through partnerships across organizations that promote mutual learning and synergy. While our guidelines represent best practices at one time, we recognize that practices evolve and need to be evaluated continuously for accuracy and relevance. Our intention is to bring awareness to these critical topics and form a foundation for important conversations surrounding equitable and inclusive research practices.

Incorporating Anti-racist Principles Throughout the Research Lifecycle: A Position Statement from the Society of General Internal Medicine (SGIM).

Biomedical research has advanced medicine but also contributed to widening racial and ethnic health inequities. Despite a growing acknowledgment of the need to incorporate anti-racist objectives into research, there remains a need for practical guidance for recognizing and addressing the influence of ingrained practices perpetuating racial harms, particularly for general internists. Through a review of the literature, and informed by the Research Lifecycle Framework, this position statement from the Society of General Internal Medicine presents a conceptual framework suggesting multi-level systemic changes and strategies for researchers to incorporate an anti-racist perspective throughout the research lifecycle. It begins with a clear assertion that race and ethnicity are socio-political constructs that have important consequences on health and health disparities through various forms of racism. Recommendations include leveraging a comprehensive approach to integrate anti-racist principles and acknowledging that racism, not race, drives health inequities. Individual researchers must acknowledge systemic racism's impact on health, engage in self-education to mitigate biases, hire diverse teams, and include historically excluded communities in research. Institutions must provide clear guidelines on the use of race and ethnicity in research, reject stigmatizing language, and invest in systemic commitments to diversity, equity, and anti-racism. National organizations must call for race-conscious research standards and training, and create measures to ensure accountability, establishing standards for race-conscious research for research funding. This position statement emphasizes our collective responsibility to combat systemic racism in research, and urges a transformative shift toward anti-racist practices throughout the research cycle.

Systematic Review Assessing Quality of the Survey Literature in Surgery.

INTRODUCTION: The use of survey methodology in surgical research has proliferated in recent years, but the quality of these surveys and of their reporting is understudied. METHODS: We conducted a comprehensive review of surgical survey literature (January 2022-July 2023) via PubMed in July 2023. Articles which (1) reported data gleaned from a survey, (2) were published in an English language journal, (3) targeted survey respondents in the United States or Canada, and (4) pertained to general surgery specialties were included. We assessed quality of survey reports using the Checklist for Reporting Of Survey Studies (CROSS) guidelines. Articles were evaluated for concordance with CROSS using a dichotomous (yes or no) scale. RESULTS: Initial literature search yielded 481 articles; 57 articles were included in analysis based on the inclusion criteria. The mean response rate was 37% (range 0.62%-98%). The majority of surveys were administered electronically (n = 50, 87.8%). No publications adhered to all 40 CROSS items; on average, publications met 61.2% of items applicable to that study. Articles were most likely to adhere to reporting criteria for title and abstract (mean adherence 99.1%), introduction (99.1%), and discussion (92.4%). Articles were least adherent to items related to methodology (42.6%) and moderately adherent to items related to results (76.6%). Only five articles cited CROSS guidelines or another standardized survey reporting tool (10.5%). CONCLUSIONS: Our analysis demonstrates that CROSS reporting guidelines for survey research have not been adopted widely. Surveys reported in surgical literature may be of variable quality. Increased adherence to guidelines could improve development and dissemination of surveys done by surgeons.

Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review.

BACKGROUND AND OBJECTIVES: Comprehending the research dataset is crucial for obtaining reliable and valid outcomes. Health analysts must have a deep comprehension of the data being analyzed. This comprehension allows them to suggest practical solutions for handling missing data, in a clinical data source. Accurate handling of missing values is critical for producing precise estimates and making informed decisions, especially in crucial areas like clinical research. With data's increasing diversity and complexity, numerous scholars have developed a range of imputation techniques. To address this, we conducted a systematic review to introduce various imputation techniques based on tabular dataset characteristics, including the mechanism, pattern, and ratio of missingness, to identify the most appropriate imputation methods in the healthcare field. MATERIALS AND METHODS: We searched four information databases namely PubMed, Web of Science, Scopus, and IEEE Xplore, for articles published up to September 20, 2023, that discussed imputation methods for addressing missing values in a clinically structured dataset. Our investigation of selected articles focused on four key aspects: the mechanism, pattern, ratio of missingness, and various imputation strategies. By synthesizing insights from these perspectives, we constructed an evidence map to recommend suitable imputation methods for handling missing values in a tabular dataset. RESULTS: Out of 2955 articles, 58 were included in the analysis. The findings from the development of the evidence map, based on the structure of the missing values and the types of imputation methods used in the extracted items from these studies, revealed that 45% of the studies employed conventional statistical methods, 31% utilized machine learning and deep learning methods, and 24% applied hybrid imputation techniques for handling missing values. CONCLUSION: Considering the structure and characteristics of missing values in a clinical dataset is essential for choosing the most appropriate data imputation technique, especially within conventional statistical methods. Accurately estimating missing values to reflect reality enhances the likelihood of obtaining high-quality and reusable data, contributing significantly to precise medical decision-making processes. Performing this review study creates a guideline for choosing the most appropriate imputation methods in data preprocessing stages to perform analytical processes on structured clinical datasets.

A deeper consideration of sex/gender in quantitative health research: a checklist for incorporating multidimensionality, variety, embodiment, and intersectionality throughout the whole research process.

BACKGROUND: There is a growing awareness of the need to adequately integrate sex and gender into health-related research. Although it is widely known that the entangled dimensions sex/gender are not comprehensively considered in most studies to date, current publications of conceptual considerations and guidelines often only give recommendations for certain stages of the research process and - to the best of our knowledge - there is a lack of a detailed guidance that accompanies each step of the entire research process. The interdisciplinary project "Integrating gender into environmental health research" (INGER) aimed to fill this gap by developing a comprehensive checklist that encourages sex/gender transformative research at all stages of the research process of quantitative health research. In the long term this contributes to a more sex/gender-equitable research. METHODS: The checklist builds on current guidelines on sex/gender in health-related research. Starting from important key documents, publications from disciplines involved in INGER were collected. Furthermore, we used a snowball method to include further relevant titles. The identification of relevant publications was continued until saturation was reached. 55 relevant publications published between 2000 and 2021 were identified, assessed, summarised and included in the developed checklist. After noticing that most publications did not cover every step of the research process and often considered sex/gender in a binary way, the recommendations were modified and enriched based on the authors' expertise to cover every research step and to add further categories to the binary sex/gender categories. RESULTS: The checklist comprises 67 items in 15 sections for integrating sex/gender in quantitative health-related research and addresses aspects of the whole research process of planning, implementing and analysing quantitative health studies as well as aspects of appropriate language, communication of results to the scientific community and the public, and research team composition. CONCLUSION: The developed comprehensive checklist goes beyond a binary consideration of sex/gender and thus enables sex/gender-transformative research. Although the project INGER focused on environmental health research, no aspects that were specific to this research area were identified in the checklist. The resulting comprehensive checklist can therefore be used in different quantitative health-related research fields.

Identifying trials run in India that are registered in other clinical trial registries: a cross-sectional study.

BACKGROUND: Clinical trials play a crucial role in biomedical research, and it is important to register them in public registries to ensure transparency and prevent research waste. In this study, we wished to determine what steps need to be taken to identify every clinical trial run in India that has been registered in any of the (non-Indian) World Health Organization-recognised primary registries. Of the 16 registries, we studied all except that of the European Union, which will be studied separately. METHODS: Two methodologies were employed for each registry, except for four that did not facilitate one or the other method. Methodology A involved downloading all the records in a registry and querying them. Methodology B involved conducting a search via the registry website. RESULTS: Only four registries provided consistent results with both methodologies. Seven registries had different results from the two methodologies. Of these, in four cases, in Methodology A one field indicated that the study ran in India, while another indicated otherwise. CONCLUSIONS: The above-mentioned ambiguities should be addressed by the concerned registries. Overall, this study reinforces the need for improved data accuracy and transparency in clinical trial registries and emphasizes the importance of resolving complications faced by users while navigating the registries. Ensuring accurate and comprehensive registration of clinical trials is essential for meta-research and the use of such data by a variety of stakeholders.

Waiving the consent requirement to mitigate bias in observational precision medicine research.

Consent bias is a type of selection bias in biomedical research where those consenting to the research differ systematically from those not consenting. It is particularly relevant in precision medicine research because the complexity of these studies prevents certain subgroups from understanding, trusting, and consenting to the research. Because consent bias distorts research findings and causes inequitable distribution of research benefits, scholars propose two types of schemes to reduce consent bias: reforming existing consent models and removing the consent requirement altogether. This study explores the possibility of waiving consent in observational studies using existing data, because they involve fewer risks to participants than clinical trials if privacy safeguards are strengthened. It suggests that data protection mechanisms such as security enhancement and data protection impact assessment should be conducted to protect data privacy of participants in observational studies without consent.