Making research articles fit for purpose: structured reporting of key methods and findings

iOntoBioethics: A Framework for the Agile Development of Bioethics Ontologies in Pandemics, Applied to COVID-19

Background: Few ontological attempts have been reported for conceptualizing the bioethics domain. In addition to limited scope representativeness and lack of robust methodological approaches in driving research design and evaluation of bioethics ontologies, no bioethics ontologies exist for pandemics and COVID-19. This research attempted to investigate whether studying the bioethics research literature, from the inception of bioethics research publications, facilitates developing highly agile, and representative computational bioethics ontology as a foundation for the automatic governance of bioethics processes in general and the COVID-19 pandemic in particular.

Research Design: The iOntoBioethics agile research framework adopted the Design Science Research Methodology. Using systematic literature mapping, the search space resulted in 26,170 Scopus indexed bioethics articles, published since 1971. iOntoBioethics underwent two distinctive stages: (1) Manually Constructing Bioethics (MCB) ontology from selected bioethics sources, and (2) Automatically generating bioethics ontological topic models with all 26,170 sources and using special-purpose developed Text Mining and Machine-Learning (TM&ML) engine. Bioethics domain experts validated these ontologies, and further extended to construct and validate the Bioethics COVID-19 Pandemic Ontology.

Results: Cross-validation of the MCB and TM&ML bioethics ontologies confirmed that the latter provided higher-level abstraction for bioethics entities with well-structured bioethics ontology class hierarchy compared to the MCB ontology. However, both bioethics ontologies were found to complement each other forming a highly comprehensive Bioethics Ontology with around 700 concepts and associations COVID-19 inclusive.

Conclusion:The iOntoBioethics framework yielded the first agile, semi-automatically generated, literature-based, and domain experts validated General Bioethics and Bioethics Pandemic Ontologies Operable in COVID-19 context with readiness for automatic governance of bioethics processes. These ontologies will be regularly and semi-automatically enriched as iOntoBioethics is proposed as an open platform for scientific and healthcare communities, in their infancy COVID-19 learning stage. iOntoBioethics not only it contributes to better understanding of bioethics processes, but also serves as a bridge linking these processes to healthcare systems. Such big data analytics platform has the potential to automatically inform bioethics governance adherence given the plethora of developing bioethics and COVID-19 pandemic knowledge. Finally, iOntoBioethics contributes toward setting the first building block for forming the field of “Bioethics Informatics”.

Cardiovascular Informatics: building a bridge to data harmony

The search for new strategies for better understanding cardiovascular disease is a constant one, spanning multitudinous types of observations and studies. A comprehensive characterization of each disease state and its biomolecular underpinnings relies upon insights gleaned from extensive information collection of various types of data. Researchers and clinicians in cardiovascular biomedicine repeatedly face questions regarding which types of data may best answer their questions, how to integrate information from multiple datasets of various types, and how to adapt emerging advances in machine learning and/or artificial intelligence to their needs in data processing. Frequently lauded as a field with great practical and translational potential, the interface between biomedical informatics and cardiovascular medicine is challenged with staggeringly massive datasets. Successful application of computational approaches to decode these complex and gigantic amounts of information becomes an essential step toward realizing the desired benefits. In this review, we examine recent efforts to adapt informatics strategies to cardiovascular biomedical research: automated information extraction and unification of multifaceted -omics data. We discuss how and why this interdisciplinary space of Cardiovascular Informatics is particularly relevant to and supportive of current experimental and clinical research. We describe in detail how open data sources and methods can drive discovery while demanding few initial resources, an advantage afforded by widespread availability of cloud computing-driven platforms. Subsequently, we provide examples of how interoperable computational systems facilitate exploration of data from multiple sources, including both consistently-formatted structured data and unstructured data. Taken together, these approaches for achieving data harmony enable molecular phenotyping of cardiovascular (CV) diseases and unification of cardiovascular knowledge.

Preterm Birth and the Development of Visual Attention During the First 2 Years of Life: A Systematic Review and Meta-analysis

Importance

Preterm birth is associated with an increased risk for long-lasting attention deficits. Early-life markers of attention abnormalities have not been established to date but could provide insights into the pathogenesis of attention abnormalities and could help identify susceptible individuals.

Objective

To examine whether preterm birth is associated with visual attention impairments in early life, and if so, in which attention functions and at which developmental period during the first 2 years of life.

Data Sources

PubMed and PsycINFO were searched on November 17, 2019, to identify studies involving visual attention outcomes in infants born preterm vs full term.

Study Selection

Peer-reviewed studies from the past 50 years met the eligibility criteria if they directly assessed visual attention outcomes until the age of 2 years in generally healthy infants born preterm or full term. The selection process was conducted by 2 independent reviewers.

Data Extraction and Synthesis

The Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guideline was followed. Random-effects models were used to determine standardized mean differences. The risk of bias was assessed both within and between studies.

Main Outcomes and Measures

Five nascent indices of visual attention were analyzed, including very basic functions-namely, the abilities to follow and fixate on visual targets-and more complex functions, such as visual processing (ie, habituation), recognition memory (ie, novelty preference), and the ability to effortfully focus attention for learning.

Results

A total of 53 studies were included, with 69 effect sizes and assessing a total of 3998 infants (2047 born preterm and 1951 born full term; of the 3376 for whom sex was reported, 1693 [50.1%] were girls). Preterm birth was associated with impairments in various attention indices, including visual-following in infancy (Cohen d, -0.77; 95% CI, -1.23 to -0.31), latency to fixate (Cohen d, -0.18; 95% CI, -0.33 to -0.02), novelty preference (Cohen d, -0.20; 95% CI, -0.32 to -0.08), and focused attention (Cohen d, -0.28; 95% CI, -0.45 to -0.11). In the neonatal period, preterm birth was associated with superior visual-following (Cohen d, 0.22; 95% CI, 0.03 to 0.40), possibly owing to the additional extrauterine exposure to sensory stimulation. However, this early association waned rapidly in infancy (Cohen d, -0.77; 95% CI, -1.23 to -0.31).

Conclusions and Relevance

The findings suggest that preterm birth is associated with impingements to visual attention development in early life, as manifested in basic and then complex forms of attention. Advancements in neonatal care may underlie improvements found in the current era and accentuate several early protective factors.

USE OF CLINDAMYCIN IN PALLAS' CATS [OTOCOLOBUS (FELIS) MANUL] TO REDUCE JUVENILE TOXOPLASMOSIS-ASSOCIATED MORTALITY RATES

Pallas' cat [Otocolobus (Felis) manul] experiences a high mortality rate from toxoplasmosis. During the period 2006-2016, the overall mortality rate for this species from all causes during the first year of life was 71.59% in European Association of Zoos and Aquaria institutions, with the most significant infectious cause from systemic toxoplasmosis (20.6%) as confirmed by postmortem examination and histopathology. Clindamycin was used starting in 2014 in two collections that had previously experienced 100% mortality rates by toxoplasmosis in kittens less than one year of age, covering key Toxoplasma gondii exposure periods for kittens (n = 17) as a prophylactic measure. This protocol resulted in a 67.03% (95% confidence interval 41.76-78.61%) reduction in the first year mortality rate over a two-year period to 5.88% in those animals treated.

Software engineering principles address current problems in the systematic review ecosystem

Systematic reviewers are simultaneously unable to produce systematic reviews fast enough to keep up with the availability of new trial evidence while overproducing systematic reviews that are unlikely to change practice because they are redundant or biased. Although the transparency and completeness of trial reporting has improved with changes in policy and new technologies, systematic reviews have not yet benefited from the same level of effort. We found that new methods and tools used to automate aspects of systematic review processes have focused on improving the efficiency of individual systematic reviews rather than the efficiency of the entire ecosystem of systematic review production. We use software engineering principles to review challenges and opportunities for improving the interoperability, integrity, efficiency, and maintainability. We conclude by recommending ways to improve access to structured systematic review results. Major opportunities for improving systematic reviews will come from new tools and changes in policy focused on doing the right systematic reviews rather than just doing more of them faster.

Biomedical text mining for research rigor and integrity: tasks, challenges, directions

An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise annually. There is growing alarm that a significant portion of this investment is wasted because of problems in reproducibility of research findings and in the rigor and integrity of research conduct and reporting. Recent years have seen a flurry of activities focusing on standardization and guideline development to enhance the reproducibility and rigor of biomedical research. Research activity is primarily communicated via textual artifacts, ranging from grant applications to journal publications. These artifacts can be both the source and the manifestation of practices leading to research waste. For example, an article may describe a poorly designed experiment, or the authors may reach conclusions not supported by the evidence presented. In this article, we pose the question of whether biomedical text mining techniques can assist the stakeholders in the biomedical research enterprise in doing their part toward enhancing research integrity and rigor. In particular, we identify four key areas in which text mining techniques can make a significant contribution: plagiarism/fraud detection, ensuring adherence to reporting guidelines, managing information overload and accurate citation/enhanced bibliometrics. We review the existing methods and tools for specific tasks, if they exist, or discuss relevant research that can provide guidance for future work. With the exponential increase in biomedical research output and the ability of text mining approaches to perform automatic tasks at large scale, we propose that such approaches can support tools that promote responsible research practices, providing significant benefits for the biomedical research enterprise.

Poor statistical reporting, inadequate data presentation and spin persist despite editorial advice

The Journal of Physiology and British Journal of Pharmacology jointly published an editorial series in 2011 to improve standards in statistical reporting and data analysis. It is not known whether reporting practices changed in response to the editorial advice. We conducted a cross-sectional analysis of reporting practices in a random sample of research papers published in these journals before (n = 202) and after (n = 199) publication of the editorial advice. Descriptive data are presented. There was no evidence that reporting practices improved following publication of the editorial advice. Overall, 76-84% of papers with written measures that summarized data variability used standard errors of the mean, and 90-96% of papers did not report exact p-values for primary analyses and post-hoc tests. 76-84% of papers that plotted measures to summarize data variability used standard errors of the mean, and only 2-4% of papers plotted raw data used to calculate variability. Of papers that reported p-values between 0.05 and 0.1, 56-63% interpreted these as trends or statistically significant. Implied or gross spin was noted incidentally in papers before (n = 10) and after (n = 9) the editorial advice was published. Overall, poor statistical reporting, inadequate data presentation and spin were present before and after the editorial advice was published. While the scientific community continues to implement strategies for improving reporting practices, our results indicate stronger incentives or enforcements are needed.

Rigor, Transparency, and Reporting Social Science Research: Why Guidelines Don't Have to Kill Your Story

Dissemination of research is the most challenging aspect of building the evidence base. Despite peer-review, evidence suggests that a substantial proportion of papers leave out details that are necessary to judge bias, consider replication, or initiate meta-analyses and systematic reviews. Reporting guidelines were created to ensure minimally adequate reporting of research and have become increasingly popular since the 1990s. There are over 200 guidelines for authors to assist in reporting a range of study methodologies. Although guidelines are freely available, they are underutilized and there is criticism regarding assumptions about methodologies targeted by guidelines. As journal editors lean into endorsements, social work authors may benefit from considering guidelines appropriate for their work. This paper explores pros and cons of guideline use by authors and journals and presents some suggestions for the field of social work, including assessment of whether profession-specific reporting guidelines are needed, and cautions regarding limitations.

Did the reporting of prognostic studies of tumour markers improve since the introduction of REMARK guideline? A comparison of reporting in published articles

Although biomarkers are perceived as highly relevant for future clinical practice, few biomarkers reach clinical utility for several reasons. Among them, poor reporting of studies is one of the major problems. To aid improvement, reporting guidelines like REMARK for tumour marker prognostic (TMP) studies were introduced several years ago. The aims of this project were to assess whether reporting quality of TMP-studies improved in comparison to a previously conducted study assessing reporting quality of TMP-studies (PRE-study) and to assess whether articles citing REMARK (citing group) are better reported, in comparison to articles not citing REMARK (not-citing group). For the POST-study, recent articles citing and not citing REMARK (53 each) were identified in selected journals through systematic literature search and evaluated in same way as in the PRE-study. Ten of the 20 items of the REMARK checklist were evaluated and used to define an overall score of reporting quality. The observed overall scores were 53.4% (range: 10%-90%) for the PRE-study, 57.7% (range: 20%-100%) for the not-citing group and 58.1% (range: 30%-100%) for the citing group of the POST-study. While there is no difference between the two groups of the POST-study, the POST-study shows a slight but not relevant improvement in reporting relative to the PRE-study. Not all the articles of the citing group, cited REMARK appropriately. Irrespective of whether REMARK was cited, the overall score was slightly higher for articles published in journals requesting adherence to REMARK than for those published in journals not requesting it: 59.9% versus 51.9%, respectively. Several years after the introduction of REMARK, many key items of TMP-studies are still very poorly reported. A combined effort is needed from authors, editors, reviewers and methodologists to improve the current situation. Good reporting is not just nice to have but is essential for any research to be useful.

Developing the DESCARTE Model: The Design of Case Study Research in Health Care

Case study is a long-established research tradition which predates the recent surge in mixed-methods research. Although a myriad of nuanced definitions of case study exist, seminal case study authors agree that the use of multiple data sources typify this research approach. The expansive case study literature demonstrates a lack of clarity and guidance in designing and reporting this approach to research. Informed by two reviews of the current health care literature, we posit that methodological description in case studies principally focuses on description of case study typology, which impedes the construction of methodologically clear and rigorous case studies. We draw from the case study and mixed-methods literature to develop the DESCARTE model as an innovative approach to the design, conduct, and reporting of case studies in health care. We examine how case study fits within the overall enterprise of qualitatively driven mixed-methods research, and the potential strengths of the model are considered.

How to spot a statistical problem: advice for a non-statistical reviewer

Statistical analyses presented in general medical journals are becoming increasingly sophisticated. BMC Medicine relies on subject reviewers to indicate when a statistical review is required. We consider this policy and provide guidance on when to recommend a manuscript for statistical evaluation. Indicators for statistical review include insufficient detail in methods or results, some common statistical issues and interpretation not based on the presented evidence. Reviewers are required to ensure that the manuscript is methodologically sound and clearly written. Within that context, they are expected to provide constructive feedback and opinion on the statistical design, analysis, presentation and interpretation. If reviewers lack the appropriate background to positively confirm the appropriateness of any of the manuscript's statistical aspects, they are encouraged to recommend it for expert statistical review.

Poor methodological detail precludes experimental repeatability and hampers synthesis in ecology

Despite the scientific method's central tenets of reproducibility (the ability to obtain similar results when repeated) and repeatability (the ability to replicate an experiment based on methods described), published ecological research continues to fail to provide sufficient methodological detail to allow either repeatability of verification. Recent systematic reviews highlight the problem, with one example demonstrating that an average of 13% of studies per year (±8.0 [SD]) failed to report sample sizes. The problem affects the ability to verify the accuracy of any analysis, to repeat methods used, and to assimilate the study findings into powerful and useful meta‐analyses. The problem is common in a variety of ecological topics examined to date, and despite previous calls for improved reporting and metadata archiving, which could indirectly alleviate the problem, there is no indication of an improvement in reporting standards over time. Here, we call on authors, editors, and peer reviewers to consider repeatability as a top priority when evaluating research manuscripts, bearing in mind that legacy and integration into the evidence base can drastically improve the impact of individual research reports.