Retracted research publications reached an all-time high in 2023, and COVID-19 publications may have higher retraction rates than other publications. To better understand the impact of COVID-19 on the research literature, we analyzed 244 retracted publications related to COVID-19 in the PubMed database and the reasons for their retraction. Peer-review manipulation (18.4%) and error (20.9%) were the most common reasons for retraction, with time to retraction occurring far more quickly than in the past (13.2 mos, compared with 32.9 mos in a 2012 study). Publications focused on controversial topics were retracted rapidly (mean time to retraction 10.8 mos) but continued to receive media attention, suggesting that retraction alone may be insufficient to prevent the spread of scientific misinformation. More than half of the retractions resulted from problems that could have been detected prior to publication, including compromise of the peer review process, plagiarism, authorship issues, lack of ethics approvals, or journal errors, suggesting that more robust screening and peer review by journals can help to mitigate the recent rise in retractions.

The scientific manuscript review process can often seem daunting and mysterious to authors. Frequently, medical journals do not describe the peer-review process in detail, which can further lead to frustration for authors, peer reviewers, and readers. This editorial describes the updated manuscript review process for Prehospital and Disaster Medicine. It is hoped that this editorial will lead to increased clarity and transparency in the review process.

Scientists have started to explore whether novel artificial intelligence (AI) tools based on large language models, such as GPT-4, could support the scientific peer review process. We sought to understand (i) whether AI versus human reviewers are able to distinguish between made-up AI-generated and human-written conference abstracts reporting on actual research, and (ii) how the quality assessments by AI versus human reviewers of the reported research correspond to each other. We conducted a large-scale field experiment during a medium-sized scientific conference, relying on 305 human-written and 20 AI-written abstracts that were reviewed either by AI or 217 human reviewers. The results show that human reviewers and GPTZero were better in discerning (AI vs. human) authorship than GPT-4. Regarding quality assessments, there was rather low agreement between both human–human and human–AI reviewer pairs, but AI reviewers were more aligned with human reviewers in classifying the very best abstracts. This indicates that AI could become a prescreening tool for scientific abstracts. The results are discussed with regard to the future development and use of AI tools during the scientific peer review process.

Blind review is ubiquitous in contemporary science, but there is no consensus among stakeholders and researchers about when or how much or why blind review should be done. In this essay, we explain why blinding enhances the impartiality and credibility of science while also defending a norm according to which blind review is a baseline presumption in scientific peer review.

As the scientific community becomes aware of low replicability rates in the extant literature, peer-reviewed journals have begun implementing initiatives with the goal of improving replicability. Such initiatives center around various rules to which authors must adhere to demonstrate their engagement in best practices. Preliminary evidence in the psychological science literature demonstrates a degree of efficacy in these initiatives. With such efficacy in place, it would be advantageous for other fields of behavioral sciences to adopt similar measures. This letter provides a discussion on lessons learned from psychological science while similarly addressing the unique challenges of other sciences to adopt measures that would be most appropriate for their field. We offer broad considerations for peer-reviewed journals in their implementation of specific policies and recommend that governing bodies of science prioritize the funding of research that addresses these measures.

Research articles in the clinical and translational science literature commonly use quantitative data to inform evaluation of interventions, learn about the etiology of disease, or develop methods for diagnostic testing or risk prediction of future events. The peer review process must evaluate the methodology used therein, including use of quantitative statistical methods. In this manuscript, we provide guidance for peer reviewers tasked with assessing quantitative methodology, intended to complement guidelines and recommendations that exist for manuscript authors. We describe components of clinical and translational science research manuscripts that require assessment including study design and hypothesis evaluation, sampling and data acquisition, interventions (for studies that include an intervention), measurement of data, statistical analysis methods, presentation of the study results, and interpretation of the study results. For each component, we describe what reviewers should look for and assess; how reviewers should provide helpful comments for fixable errors or omissions; and how reviewers should communicate uncorrectable and irreparable errors. We then discuss the critical concepts of transparency and acceptance/revision guidelines when communicating with responsible journal editors.

There is currently a heightened need for transparency in pharmaceutical sectors. The inclusion of real-world (RW) evidence, in addition to clinical trial evidence, in decision-making processes, was an important step forward toward a more inclusive established value proposition. This advance has introduced new transparency challenges. Increasing transparency is a critical step toward accelerating improvement in type, quality, and access to data, regardless of whether these originate from clinical trials or from RW studies. However, so far, advances in transparency have been relatively restricted to clinical trials, and there remains a lack of similar expectations or standards of transparency concerning the generation and reporting of RW data. This perspective paper aims to highlight the need for transparency concerning RW studies, data, and evidence across health care sectors, to identify areas for improvement, and provide concrete recommendations and practices for the future. Specific issues are discussed from different stakeholder perspectives, culminating in recommended actions, from individual stakeholder perspectives, for improved RW study, data, and evidence transparency. Furthermore, a list of potential guidelines for consideration by stakeholders is proposed. While recommendations from different stakeholder perspectives are made, true transparency in the processes involved in the generation, reporting, and use of RW evidence will require a concerted effort from all stakeholders across health care sectors.