The Systematic Review Data Repository (SRDR): descriptive characteristics of publicly available data and opportunities for research

Nine quick tips for open meta-analyses

Open science principles are revolutionizing the transparency, reproducibility, and accessibility of research. Meta-analysis has become a key technique for synthesizing data across studies in a principled way; however, its impact is contingent on adherence to open science practices. Here, we outline 9 quick tips for open meta-analyses, aimed at guiding researchers to maximize the reach and utility of their findings. We advocate for outlining preregistering clear protocols, opting for open tools and software, and the use of version control systems to ensure transparency and facilitate collaboration. We further emphasize the importance of reproducibility, for example, by sharing search syntax and analysis scripts, and discuss the benefits of planning for dynamic updating to enable living meta-analyses. We also recommend publication in open-access formats, as well as open data, open code, and open access publication. We close by encouraging active promotion of research findings to bridge the gap between complex syntheses and public discourse, and provide a detailed submission checklist to equip researchers, reviewers and journal editors with a structured approach to conducting and reporting open meta-analyses.

What is the association between the microbiome and cognition? An umbrella review protocol

INTRODUCTION

Cognitive impairment is reported in a variety of clinical conditions including Alzheimer's disease, Parkinson's and 'long-COVID'. Interestingly, many of these clinical conditions are also associated with microbial dysbiosis. This comanifestation of cognitive and microbiome findings in seemingly unrelated maladies suggests that they could share a common mechanism and potentially presents a treatment target. Although a rapidly growing body of literature has documented this comorbid presentation within specific conditions, an overview highlighting potential parallels across healthy and clinical populations is lacking. The objective of this umbrella review, therefore, is to summarise and synthesise the findings of these systematic reviews.

METHODS AND ANALYSIS

On 2 April 2023, we searched MEDLINE (Pubmed), Embase (Ovid), the Web of Science (Core Collection), the Cochrane Library of Systematic Reviews and Epistemonikos as well as grey literature sources, for systematic reviews on clinical conditions and interventions where cognitive and microbiome outcomes were coreported. An updated search will be conducted before completion of the project if the search-to-publication date is >1 year old. Screening, data abstraction and quality assessment (AMSTAR 2, A MeaSurement Tool to Assess systematic Reviews) will be conducted independently and in duplicate, with disagreements resolved by consensus. Evidence certainty statements for each review's conclusions (eg, Grading of Recommendations Assessment, Development and Evaluation (GRADE)) will be extracted or constructed de novo. A narrative synthesis will be conducted and delineated by the review question. Primary study overlap will be visualised using a citation matrix as well as calculated using the corrected covered area method.

ETHICS AND DISSEMINATION

No participant-identifying information will be used in this review. No ethics approval was required due to our study methodology. Our findings will be presented at national and international conferences and disseminated via social media and press releases. We will recruit at least one person living with cognitive impairment to collaborate on writing the plain language summary for the review.

PROSPERO REGISTRATION NUMBER

CRD42023412903.

Interventions for Dry Eye: An Overview of Systematic Reviews

Importance

Dry eye is a common ocular disease that can have substantial morbidity. Systematic reviews provide evidence for dry eye interventions and can be useful for patients, clinicians, and clinical guideline developers. Overviews of reviews use explicit and systematic methods to synthesize findings from multiple systematic reviews, but currently, there are no overviews of systematic reviews investigating interventions for dry eye.

Objective

To summarize the results of reliable systematic reviews of dry eye interventions and to highlight the evidence gaps identified.

Evidence Review

We searched the Cochrane Eyes and Vision US satellite database and included reliable systematic reviews evaluating dry eye interventions published from 2016 to 2022. We reported the proportion of systematic reviews that were reliable with reasons for unreliability. Critical and important outcomes from reliable systematic reviews were extracted and verified. Critical outcomes included dry eye-related patient-reported outcome measures. Results were synthesized from reliable systematic reviews to provide summaries of evidence for each intervention. Evidence for each intervention was defined as conclusive or inconclusive depending on whether high-certainty evidence across systematic reviews was available according to Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) criteria and whether findings reached statistical or clinical significance. Recommendations were made for further research.

Findings

Within the Cochrane Eyes and Vision US satellite database, 138 potentially relevant systematic reviews were identified, 71 were considered eligible, and 26 (37%) were assessed as reliable. Among reliable systematic reviews, no conclusive evidence was identified for any dry eye intervention. Inconclusive evidence suggested that environmental modifications, dietary modifications, artificial tears and lubricants, punctal occlusion, intense pulsed light therapy, vectored thermal pulsation therapy (Lipiflow), topical corticosteroids, topical cyclosporine A, topical secretagogues, and autologous serum may be effective. Only unreliable systematic reviews evaluated lifitegrast, oral antibiotics, and moisture chamber devices.

Conclusions and Relevance

This overview of systematic reviews found some evidence that dry eye interventions may be effective, but no conclusive evidence was available. The conduct and reporting of most systematic reviews for dry eye interventions warrant improvement, and reliable systematic reviews are needed to evaluate lifitegrast, oral antibiotics, and moisture chamber devices.

The benefits of data sharing and ensuring open sources of systematic review data

AIMS

The benefits of increasing public access to data from clinical trials are widely accepted. Such benefits extend to the sharing of data from high-quality systematic reviews, given the time and cost involved with undertaking reviews. We describe the application of open sources of review data, outline potential challenges and highlight efforts made to address these challenges, with the intent of encouraging publishers, funders and authors to consider sharing review data more broadly.

RESULTS

We describe the application of systematic review data in: (i) advancing understanding of clinical trials and systematic review methods, (ii) repurposing of data to answer public health policy and practice relevant questions, (iii) identification of research gaps and (iv) accelerating the conduct of rapid reviews to inform decision making. While access, logistical, motivational and legal challenges exist, there has been progress made by systematic review, academic and funding agencies to incentivise data sharing and create infrastructure to support greater access to systematic review data.

CONCLUSION

There is opportunity to maximize the benefits of research investment in undertaking systematic reviews by ensuring open sources of systematic review data. Efforts to create such systems should draw on learnings and principles outlined for sharing clinical trial data.

GAN-Based Approaches for Generating Structured Data in the Medical Domain

Modern machine and deep learning methods require large datasets to achieve reliable and robust results. This requirement is often difficult to meet in the medical field, due to data sharing limitations imposed by privacy regulations or the presence of a small number of patients (e.g., rare diseases). To address this data scarcity and to improve the situation, novel generative models such as Generative Adversarial Networks (GANs) have been widely used to generate synthetic data that mimic real data by representing features that reflect health-related information without reference to real patients. In this paper, we consider several GAN models to generate synthetic data used for training binary (malignant/benign) classifiers, and compare their performances in terms of classification accuracy with cases where only real data are considered. We aim to investigate how synthetic data can improve classification accuracy, especially when a small amount of data is available. To this end, we have developed and implemented an evaluation framework where binary classifiers are trained on extended datasets containing both real and synthetic data. The results show improved accuracy for classifiers trained with generated data from more advanced GAN models, even when limited amounts of original data are available.

Systematic reviews should be at the heart of continuing medical education

Today, keeping up with the fast evolving evidence is more challenging than ever for practising physicians. A huge number of studies are published every day, and it is no longer possible to read all the relevant individual studies. Many physicians prefer attending continuing medical education (CME) to reading international scientific publications. Consequently, it is critical that CME is based on the best available evidence and presented in an unbiased manner free of conflicts of interest. Systematic reviews and Cochrane reviews in particular can thus provide a valuable resource of up-to-date and high-quality information on health care questions for CME providers. Of note, systematic reviews might become outdated quickly. Furthermore, some systematic reviews are fraught with limitations such as poor methodology and conduct or incomplete and misleading reporting. This article provides a brief overview of systematic reviews and Cochrane reviews, outlines how systematic reviews can be “kept alive” using today’s digital opportunities and points to several common problems of systematic reviews with suggestions for solutions.

Data Management for Systematic Reviews: Guidance is Needed

Data management practices for systematic reviews and other types of knowledge syntheses are variable, with some reviews following open science practices and others with poor reporting practices leading to lack of transparency or reproducibility. Reporting standards have improved the level of detail being shared in published reviews, and also encourage more open sharing of data from various stages of the review process. Similar to project planning or completion of an ethics application, systematic review teams should create a data management plan alongside creation of their study protocol. This commentary provides a brief description of a Data Management Plan Template created specifically for systematic reviews. It also describes the companion LibGuide which was created to provide more detailed examples, and to serve as a living document for updates and new guidance. The creation of the template was funded by the Portage Network.

PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews

The methods and results of systematic reviews should be reported in sufficient detail to allow users to assess the trustworthiness and applicability of the review findings. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement was developed to facilitate transparent and complete reporting of systematic reviews and has been updated (to PRISMA 2020) to reflect recent advances in systematic review methodology and terminology. Here, we present the explanation and elaboration paper for PRISMA 2020, where we explain why reporting of each item is recommended, present bullet points that detail the reporting recommendations, and present examples from published reviews. We hope that changes to the content and structure of PRISMA 2020 will facilitate uptake of the guideline and lead to more transparent, complete, and accurate reporting of systematic reviews.

Biomedical Data Sharing Among Researchers: A Study from Jordan

Background

Data sharing is an encouraged practice to support research in all fields. For that purpose, it is important to examine perceptions and concerns of researchers about biomedical data sharing, which was investigated in the current study.

Methods

This is a cross-sectional survey study that was distributed among biomedical researchers in Jordan, as an example of developing countries. The study survey consisted of questions about demographics and about respondent’s attitudes toward sharing of biomedical data.

Results

Among study participants, 46.9% (n=82) were positive regarding making their research data available to the public, whereas 53.1% refused the idea. The reasons for refusing to publicly share their data included “lack of regulations” (33.5%), “access to research data should be limited to the research team” (29.5%), “no place to deposit the data” (6.5%), and “lack of funding for data deposition” (6.0%). Agreement with the idea of making data available was associated with academic rank (P=0.003). Moreover, gender (P-value=0.043) and number of publications (P-value=0.005) were associated with a time frame for data sharing (ie, agreeing to share data before vs after publication).

Conclusion

About half of the respondents reported a positive attitude toward biomedical data sharing. Proper regulations and facilitation data deposition can enhance data sharing in Jordan.