Editorial bias in scientific publications

Status bias in Chinese scholarly publishing: an exploratory study based on mixed methods

The academic community requires not only responsible research but also responsible academic journals. An exploratory study of Chinese-language academic journals that used mixed methods found Chinese-language academic journals on the humanities and social sciences exhibiting a widespread status bias. Most of them summarily rejected submissions from junior researchers and students without paying due attention to the quality of the research itself. The main reasons for this problem are editorial department resources, the scientific research evaluation system, the editorial department culture, and the wider academic environment. This study recommends that Chinese-language academic journals join the Committee on Publication Ethics, other publishing ethics organizations, and the "Responsible Journals" program as soon as possible.

ChatGPT identifies gender disparities in scientific peer review

The peer review process is a critical step in ensuring the quality of scientific research. However, its subjectivity has raised concerns. To investigate this issue, I examined over 500 publicly available peer review reports from 200 published neuroscience papers in 2022-2023. OpenAI's generative artificial intelligence ChatGPT was used to analyze language use in these reports, which demonstrated superior performance compared to traditional lexicon- and rule-based language models. As expected, most reviews for these published papers were seen as favorable by ChatGPT (89.8% of reviews), and language use was mostly polite (99.8% of reviews). However, this analysis also demonstrated high levels of variability in how each reviewer scored the same paper, indicating the presence of subjectivity in the peer review process. The results further revealed that female first authors received less polite reviews than their male peers, indicating a gender bias in reviewing. In addition, published papers with a female senior author received more favorable reviews than papers with a male senior author, for which I discuss potential causes. Together, this study highlights the potential of generative artificial intelligence in performing natural language processing of specialized scientific texts. As a proof of concept, I show that ChatGPT can identify areas of concern in scientific peer review, underscoring the importance of transparent peer review in studying equitability in scientific publishing.

Would Moving Forward Mean Going Back? Comment on Maselli et al. Direct Access to Physical Therapy: Should Italy Move Forward? Int. J. Environ. Res. Public Health 2022, 19, 555

In a recent communication, Maselli and colleagues [...].

Mitigating Biases in a Cohort Study: A Critical Examination of the Silicone Arthroplasty in Rheumatoid Arthritis Study

Research bias, or the systematic errors of a study, can arise before, during, or after a trial ends. These biases hinder the internal validity of the study, which is the accuracy of a study's conclusions regarding the effects of an intervention on a given group of subjects. With the growing use of evidence-based medicine, there is a demand for high-quality evidence from the research community. Systematic reviews and meta-analyses of randomized controlled trials are considered the highest level of evidence, followed by individual randomized controlled trials. However, most surgical trials cannot be conducted as randomized controlled trials because of factors such as patient preferences and lack of equipoise among surgeons. Therefore, surgical trials may lack features that are held as important standards for high-quality evidence, such as randomization and blinding. To demonstrate the biases that surgical trials may encounter, the authors examined a prospective cohort study, the Silicone Arthroplasty in Rheumatoid Arthritis study. The authors focus on the challenges that arise during a surgical trial, including the design, implementation, and methods used to report the clinical evidence. By recognizing and addressing obstacles that exist in research, investigators will provide health care providers with high-quality evidence needed to make well-informed, evidence-based clinical decisions.

Improving the ACGIH threshold limit value (TLV) process

The intent of this manuscript is to elucidate needed improvements in American Conference of Governmental Industrial Hygienists (ACGIH) threshold limit value (TLV) deliberations. More broadly, irreproducibility and bias adversely impact the collection, interpretation, statistical analysis, presentation, and reporting of results in many fields. In 2012, Begley and Ellis reported that scientists at Amgen had attempted to confirm published findings related to research topics of possible interest to Amgen. Fifty-three papers were deemed “landmark” studies. The authors were “shocked” when scientific findings were confirmed in only 6 (11%) cases. Many studies have confirmed that the peer-reviewed literature in biomedicine is in the midst of an irreproducibility crisis. Compounding the irreproducibility crisis is the existence of a significant bias against the publication of negative results. In the toxicology setting, negative toxicity test results are infrequently published as compared with reports that a chemical possesses a particular toxicity in a given test. Despite these deficiencies, the ACGIH states that “…the TLV®-CS Committee preferably relies on published, peer reviewed literature available in the public domain.” The primarily academic studies published in the peer-reviewed literature upon which ACGIH relies to determine TLVs rarely report raw data not already statistically transformed that are thus incalculable. In contrast, consideration of unpublished studies funded by industry, the vast majority of which are good laboratory practice-conducted contract lab studies, is only acceptable to ACGIH if the data owner provides the raw data to third parties upon request. This asymmetry in both the source of data emphasized, and inability to independently statistically analyze findings reported in the published academic literature, introduces a strong skew toward reliance on unverifiable although published measurements in the TLV process. Since Occupational Safety and Health Administration (OSHA) recommends that workplaces rely on ACGIH TLVs and National Institute for Occupational Safety and Health recommended exposure limits rather than older OSHA permissible exposure limit values to optimize worker safety, ACGIH should adopt a more transparent and science-based process.

US studies may overestimate effect sizes in softer research

Many biases affect scientific research, causing a waste of resources, posing a threat to human health, and hampering scientific progress. These problems are hypothesized to be worsened by lack of consensus on theories and methods, by selective publication processes, and by career systems too heavily oriented toward productivity, such as those adopted in the United States (US). Here, we extracted 1,174 primary outcomes appearing in 82 meta-analyses published in health-related biological and behavioral research sampled from the Web of Science categories Genetics & Heredity and Psychiatry and measured how individual results deviated from the overall summary effect size within their respective meta-analysis. We found that primary studies whose outcome included behavioral parameters were generally more likely to report extreme effects, and those with a corresponding author based in the US were more likely to deviate in the direction predicted by their experimental hypotheses, particularly when their outcome did not include additional biological parameters. Nonbehavioral studies showed no such "US effect" and were subject mainly to sampling variance and small-study effects, which were stronger for non-US countries. Although this latter finding could be interpreted as a publication bias against non-US authors, the US effect observed in behavioral research is unlikely to be generated by editorial biases. Behavioral studies have lower methodological consensus and higher noise, making US researchers potentially more likely to express an underlying propensity to report strong and significant findings.

Publication bias in neuroimaging research: implications for meta-analyses

Neuroimaging and the neurosciences have made notable advances in sharing activation results through detailed databases, making meta-analysis of the published research faster and easier. However, the effect of publication bias in these fields has not been previously addressed or accounted for in the developed meta-analytic methods. In this article, we examine publication bias in functional magnetic resonance imaging (fMRI) for tasks involving working memory in the frontal lobes (Brodmann Areas 4, 6, 8, 9, 10, 37, 45, 46, and 47). Seventy-four studies were selected from the literature and the effect of publication bias was examined using a number of regression-based techniques. Pearson's r correlation coefficient and Cohen's d effect size estimates were computed for the activation in each study and compared to the study sample size using Egger's regression, Macaskill's regression, and the 'Trim and Fill' method. Evidence for publication bias was identified in this body of literature (p < 0.01 for each test), generally, though was neither task- nor sub-region-dependent. While we focused our analysis on this subgroup of brain mapping studies, we believe our findings generalize to the brain imaging literature as a whole and databases seeking to curate their collective results. While neuroimaging databases of summary effects are of enormous value to the community, the potential publication bias should be considered when performing meta-analyses based on database contents.

Shadow enhancers: frequently asked questions about distributed cis-regulatory information and enhancer redundancy

This paper, in the form of a frequently asked questions page (FAQ), addresses outstanding questions about "shadow enhancers", quasi-redundant cis-regulatory elements, and their proposed roles in transcriptional control. Questions include: What exactly are shadow enhancers? How many genes have shadow/redundant/distributed enhancers? How redundant are these elements? What is the function of distributed enhancers? How modular are enhancers? Is it useful to study a single enhancer in isolation? In addition, a revised definition of "shadow enhancers" is proposed, and possible mechanisms of shadow enhancer function and evolution are discussed.

Ghost-authors, improvement article communication, and medical publications

INTRODUCTION

Relationship between ghost-authors and medical writers has been debated, even arriving to citizens. Accusation that through ghost-authors, market messages are introduced in medical journals has been suggested.

DEVELOPMENT

This paper carried out an analysis of ghost-authors and their application to medical writers and discusses the intellectual authorship of the scientific articles, as well as the relationship with research groups and pharmaceutical industry, as well as the position of journal.

CONCLUSIONS

The role of medical workers is advisable because improves communication of articles. Using authorship criteria, medical writers cannot be considered as ghost-authors. Misconducts for medical writers should be pursued but also those from editors and authors. Responsibility of articles belongs to authors who are responsible of false conclusions obtained from data research and to the editor in the case of the paper was published.