Search bioRxiv⌕ Search

bioRxiv · 10.1101/106757

Concern noted: A descriptive study of editorial expressions of concern in PubMed and PubMed Central.

Abstract

BackgroundAn editorial expression of concern (EEoC) is issued by editors or publishers to draw attention to potential problems in a publication, without itself constituting a retraction or correction.\n\nMethodsWe searched PubMed, PubMed Central (PMC), and Google Scholar to identify EEoCs issued for publications in PubMed and PMC up to 22 August 2016. We also searched the archives of the Retraction Watch blog, some journal and publisher websites, and studies of EEoCs. In addition, we searched for retractions of EEoCs and affected articles in PubMed up to 8 December 2016. We analyzed overall historical trends, as well as reported reasons and subsequent editorial actions related to EEoCs issued between August 2014 and August 2016.\n\nResultsAfter screening 5,076 records, we identified 230 EEoCs that affect 300 publications indexed in PubMed, the earliest issued in 1985. Half of the primary EEoCs were issued between 2014 and 2016 (52%). We found evidence of some EEoCs that had been removed by the publisher without leaving a record and some were not submitted for PubMed or PMC indexing. A minority of publications affected by EEoCs had been retracted by early December 2016 (25%). For the subset of 92 EEoCs issued between August 2014 and August 2016, affecting 99 publications, the rate of retraction was similar (29%). The majority of EEoCs were issued because of concerns with validity of data, methods, or interpretation of the publication (68%), and 31% of cases remained open. Issues with images were raised in 40% of affected publications. Ongoing monitoring after the study identified another 17 EEoCs to years end in 2016, increasing the number of EEoCs to 247 and publications in PubMed known to be affected by EEoCs to 320 at the end of 2016.\n\nConclusionsEEoCs have been rare publishing events in the biomedical literature, but their use has been increasing. Most have not led to retractions, and many remain unresolved. Lack of prominence and inconsistencies in management of EEoCs reduce the ability of these notices to alert the scientific community to potentially serious problems in publications. EEoCs will be made identifiable in PubMed in 2017.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Vaught, M., Jordan, D. C., Bastian, H.. 2017-02-27. Concern noted: A descriptive study of editorial expressions of concern in PubMed and PubMed Central.. https://doi.org/10.1101/106757

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

From Problems to Solutions: How Self-Questions Drive Strong Scientific Reasoning

Metacognition, or awareness and control of thinking, is essential for problem solving because it helps solvers select strategies for learning and decide when to enact them (planning), check their understanding in the moment (monitoring), and assess effectiveness of solutions and learning strategies (evaluating). Most studies that examine metacognition do so retrospectively, leaving a critical gap in how metacognition is used in-the-moment for problem solving. In this study, we utilized a think-aloud interview approach to capture and characterize the metacognitive skills that senior undergraduate life science students at three institution types in the United States used spontaneously while solving challenging biology problems. We found that the students (n = 39) displayed a wide variety and quantity of monitoring, some evaluating, and nearly no planning skills while creating a solution backed with high-quality reasoning. Students frequently used self-questions to monitor and evaluate their thinking. We found that self-questioning is a critical skill for generating solutions, but it only moves problem solving forward if the student asks and correctly answers the self-question out loud. This study illustrates how self-questioning and other metacognitive regulation skills provide links to bridge the gap between a student's prior knowledge and specific features of a challenging problem. Based on our student data, we conceptualized a model of metacognitive phases during problem solving with high-quality reasoning. Finally, we present a self-question scaffold to facilitate the proposed phases (planning, monitoring, reasoning, and evaluating) so educators can equip students with metacognitive and scientific reasoning tools for effective problem solving.

scientific communication and education↗

Preprint solicitation as a natural experiment in scholarly publishing

Preprinting is a growing and increasingly recognized mechanism for scientific communication and productivity and a way to facilitate global access to science. The interplay of preprinting practices with formal peer-reviewed publication is an open question that is now tractable to targeted analysis. Here, we take advantage of nearly a decade of preprint solicitation at Proceedings B to address this question, which offers a quasi-experimental setting for comparing outcomes for solicited preprints and traditionally submitted manuscripts. We used submission records from 15,368 manuscripts from 2018-2024 to assess whether preprint solicitation affects acceptance rates, journal scope, author geographic diversity, and post-publication uptake. Solicited manuscripts were accepted at nearly twice the rate of traditional submissions and received more downloads and citations after publication. Submission topics were broadly similar between the two routes, though solicited manuscripts showed marginally greater subject diversity. Geographic disparities favoring the Global North persisted, but acceptance rates for solicited manuscripts converged across regions, narrowing the gap seen in traditional submissions. These findings extend earlier work on this initiative and indicate that editor-driven solicitation can identify high-quality preprints, broaden geographic representation, and increase research visibility, offering a replicable model for journals seeking to diversify authorship while maintaining editorial standards.

scientific communication and education↗

Biology Undergraduates Use of AI-Enabled Feedback to Revise Scientific Writing: Recipience and Barriers

Background: Instructors and education researchers are increasingly developing feedback with the assistance of generative AI, which we refer to as AI-enabled feedback, yet we do not know whether established understandings of feedback uptake and effectiveness apply similarly in AI-mediated contexts. This makes the recipience of AI-enabled feedback a central concern to understanding how to best support students in developing their scientific writing skills. This study examined how two teams of undergraduate biology students used AI-enabled feedback to revise their scientific writing, and the extent to which their revisions addressed that feedback. The teams revised their research proposal drafts after receiving AI-enabled feedback developed by their instructor with the aid of a custom AI tool. Results: Our findings show that AI-enabled feedback prompted actionable revision across both teams. Each team addressed approximately half of the feedback comments, indicating comparable levels of uptake despite their differing approaches to revision. We observed that both teams were inconsistent in providing the additional information and reasoning requested in the feedback, and they struggled to address comments requiring self-evaluation. Taken together, we see that students often addressed the more easily actionable parts of AI-enabled feedback while omitting more demanding revisions. Conclusions: These findings provide behavioral evidence of how students used AI-enabled feedback to revise their scientific writing. The observed patterns suggest that the content of AI-enabled feedback supported meaningful revision, while limitations in students agency, strategies, and volition likely shaped their implementation of comments. Although the degree of uptake observed in this study is encouraging, it is unlikely that a single application of generative AI within the feedback process will resolve all the challenges associated with feedback uptake.

scientific communication and education↗