bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.01.23.477400

Correction of the scientific production: publisher performance evaluation using a dataset of 4844 PubMed retractions.

Toma, C. S.·Padureanu, L.·Toma, B.
View original source ↗Export metadata ↓

Abstract

BackgroundWithdrawal of problematic scientific articles after publication is one of the mechanisms for correcting the literature available to publishers, especially in the conditions of the ever-increasing trend of publishing activity in the medical field. The market volume and the business model justify publishers involvement in the post-publication quality control(QC) of scientific production. The limited information about this subject determined us to analyze retractions and the main retraction reasons for publishers with many withdrawn articles. We also propose a score to measure the evolution of their performance. The data set used for this article consists of 4844 PubMed retracted papers published between 1.01.2009 and 31.12.2020. MethodsWe have analyzed the retraction notes and retraction reasons, grouping them by publisher. To evaluate performance, we formulated an SDTP score whose calculation formula includes several parameters: speed (article exposure time(ET)), detection rate (percentage of articles whose retraction is initiated by the editor/publisher/institution without the authors participation), transparency (percentage of retracted articles available online and clarity of retraction notes), precision (mention of authors responsibility and percentage of retractions for reasons other than editorial errors). ResultsThe 4844 withdrawn articles were published in 1767 journals by 366 publishers, the average number of withdrawn articles/journal being 2.74. Forty-five publishers have more than ten withdrawn articles, holding 88% of all papers and 79% of journals. Combining our data with data from another study shows that less than 7% of PubMed journals withdrew at least one article. Only 10.5% of the withdrawal notes included the individual responsibility of the authors. Nine of the top 11 publishers had the largest number of articles withdrawn in 2020, in the first 11 places finding, as expected, some big publishers. Retraction reasons analysis shows considerable differences between publishers concerning the articles ET: median values between 9 and 43 months (mistakes), 9 and 73 months (images), 10 and 42 months (plagiarism & overlap). The SDTP score shows, between 2018 and 2020, an improvement in QC of four publishers in the top 11 and a decrease in the gap between 1st and 11th place. The group of the other 355 publishers also has a positive evolution of the SDTP score. ConclusionsPublishers have to get involved actively and measurably in the post-publication evaluation of scientific products. The introduction of reporting standards for retraction notes and replicable indicators for quantifying publishing QC can help increase the overall quality of scientific literature.

Source connections

Explore related subjects

scientific communication and educationPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Toma, C. S., Padureanu, L., Toma, B.. 2022-01-25. Correction of the scientific production: publisher performance evaluation using a dataset of 4844 PubMed retractions.. https://doi.org/10.1101/2022.01.23.477400

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

KEEP EXPLORING

Related preprints

bioRxiv2026

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗
bioRxiv2022

Discussion-based DEI education to help create inclusive and open BME research lab environments

Diversity in teams has been shown to enhance creativity and innovation, particularly in teams where all members felt a sense of belonging. Creating an inclusive environment in a lab setting that provides a sense of belonging to all is challenging. This is particularly true in a field like Biomedical Engineering/Bioengineering where diversity is multifaceted and includes peoples diverse personal/cultural backgrounds and also diverse scientific backgrounds. In a research lab, there is additional diversity in training, as most labs contain trainees at different levels (undergrad, grad, postdoc, high school). To aid in creating a sense of belonging in a research lab, we have devised a novel initiative based upon open group discussions on diversity, equity, and inclusion topics. The initiative included a first presentation/discussion by the PI to set the stage for defining diversity, equity, and inclusion, and provided examples of existing DEI issues within our field, such as lack of racial diversity in awards like the NIH New Innovator Award and among conference speakers. After the initial presentation, trainee-led, bi-weekly-structured discussions were maintained. First, discussions focused broadly on any DEI related topic to enhance general knowledge on DEI issues and provide discussion comfort among the group. Second, there was a period to deepen knowledge of a specific topic (in our example, microaggressions), which was followed up with a period of discussions on potential solutions (such as how to react when observing a microaggression and what to do in response to realizing ones own microaggression). Students reported that our discussions were the only ones they have had in their training thus far, and felt that these discussions made them feel like they belonged in the lab, made the lab more inclusive, enhanced their awareness of how to create inclusive spaces, and taught them about a variety of DEI topics. Overall, our DEI discussions have fostered open conversations within our lab group about DEI-related issues and topics at the lab, department, university, and countrywide levels and has established a space where students feel safe to voice their opinions and ask questions. We hope to ultimately use these DEI discussions to create actionable steps for addressing topics, e.g., microaggressions, in different scenarios that can be applied by group members in their future careers.

scientific communication and education↗
bioRxiv2022

Defining Predictors of Successful Early Career to Independent Funding Conversion Among Surgeon-Scientists

IntroductionThe National Institutes of Health (NIH) provides research funding to scientists at different stages of their career through a range of grant awards. Early-stage researchers are eligible for mentored Career Development (K) awards, to aid in the transition to independent NIH funding. Factors such as education, subspecialty, and time to funding have been studied as predictors of obtaining independent awards in nonsurgical specialties. However, in surgery, the importance of these factors has yet to be clearly elucidated. We aim to identify predictors of K to independent award conversion among surgeon-scientists to understand how to better support early-stage researchers transitioning to independent careers. Materials and MethodsIn July 2020, the NIH Research Portfolio Online Reporting Tools database was queried for individuals affiliated with surgery departments who received NIH Career Development Awards (between 2000 and 2020). The following factors were analyzed: publications, institution, degrees, year of completion of training, and gender. ResultsBetween 2000 and 2020, 228 surgeons received K Awards, of which 44% transitioned to independent funding. On average, surgeons received a K award 4.0 years after completing fellowship training and an independent award 5.4 years after receiving a K grant. The time to receiving a K award was predictive of successfully achieving independent funding, and those with independent funding had a significantly greater number of publications per year of their K-award. ConclusionSurgeons successful in transitioning to independent NIH awards do so approximately 9 years after finishing fellowship. Publication track record is the main factor associated with successful conversion from a K award. Surgery departments should emphasize manuscript productivity and develop strategies to minimize time to independent funding to help K-awardees begin independent research careers.

scientific communication and education↗