United States

Fares Alahdab,&lt;sup&gt;1,2,3&lt;/sup&gt; Juan Franco,&lt;sup&gt;3,4&lt;/sup&gt; Helen Macdonald,&lt;sup&gt;4&lt;/sup&gt; Sara Schroter&lt;sup&gt;4&lt;/sup&gt;
&lt;h4&gt;Objective&lt;/h4&gt;
Peer reviewer fatigue is on the rise, as reviewers are overburdened with increasing tasks and manuscripts to review, making it challenging for journal editors to secure a sufficient number of high-quality reviews.&lt;sup&gt;1&lt;/sup&gt; Despite the potential benefits of using large language models (LLMs) in editorial and peer review processes&lt;sup&gt;2&lt;/sup&gt; and the potential to support reviewers with tasks, it is not yet known how good they are at peer review. Additionally, covert use of LLMs in peer review is increasingly suspected, with limited knowledge of positive or negative impact. We aim to compare the quality and comprehensiveness of peer reviews produced by 5 LLMs compared with peer reviewers for research submissions.
&lt;h4&gt;Design&lt;/h4&gt;
A comparative study of peer reviews produced by LLMs (GPT4o, GPTo3, Claude 3.5, Gemini 1.5 Pro, and Gemini 2.0 Flash) compared with 2 human peer reviews for research submissions between May 2024 and June 2025. Manuscripts were uploaded to Google’s Vertex AI, a private and secure BMJ Group workspace for using LLMs; the same prompt was used for all submissions (single-shot prompting). An experienced editor performed the ratings of all LLM and human reviews using the Review Quality Instrument (RQI),&lt;sup&gt;3&lt;/sup&gt; a tool for assessment of review quality comprising 8 questions, each on a 5-point Likert scale. Secondary outcomes include a comprehensiveness score, based on elements of the manuscript that the reviews focused on, and whether they were evaluative in nature. Additionally, once the first decision has been made on the manuscripts, we plan to invite authors to complete a survey about their perceptions of both the LLM review reports and the peer reviewer reports for their manuscripts. We also plan to have a second editor perform the ratings and compute interrater agreement. Reviews produced by LLMs are not included in the decision-making for the manuscripts. Wilcoxon rank sum tests were used to compare LLM and human scores for each RQI item.

&lt;h4&gt;Results&lt;/h4&gt; Preliminary data include 35 reviews (25 by LLMs and 10 by peer reviewers) of 5 BMJ submissions. Across 8 RQI items, LLM-generated reviews had higher mean (SD) scores than human reviewers on identifying strengths and weaknesses (4.12 [0.53] vs 2.70 [0.95]; P &amp;lt; 0.001); providing useful comments on the writing, organization, tables, and figures (3.72 [0.79] vs 1.80 [0.63]; P &amp;lt; .001); and constructiveness (4.00 [0.58] vs 3.00 [1.05]; P = .004) (&lt;b&gt;Table 25-1146&lt;/b&gt;). Complete data will include 200 submissions to 4 BMJ journals, the secondary outcomes, and authors’ feedback on the LLM reviews.

![](https://assets.underline.io/markdown_image/1/image/b23f8733c131796f4e73f037ea693a62.png)

&lt;h4&gt;Conclusions&lt;/h4&gt;
LLM-generated reviews matched or exceeded human reviewers on a few key dimensions of review quality. A fuller analysis will shed more light on the potential value of LLM peer reviews and how they could complement human peer reviewers’ work.
&lt;h4&gt;References&lt;/h4&gt;
1. Publons. Global State of Peer Review report. &lt;a href=&quot;https://publons.com/community/gspr&quot;&gt;https://publons.com/community/gspr&lt;/a&gt;

2. Liang W, Zhang Y, Cao H, et al. Can large language models provide useful feedback on research papers? a large-scale empirical analysis. &lt;i&gt;arXiv. &lt;/i&gt;Preprint posted October 2,&lt;i&gt; &lt;/i&gt;2023. &lt;a href=&quot;https://doi.org/10.48550/arXiv.2310.01783&quot;&gt;doi.org/10.48550/arXiv.2310.01783&lt;/a&gt;

3. van Rooyen S, Black N, Godlee F. Development of the review quality instrument (RQI) for assessing peer reviews of manuscripts.&lt;i&gt; J Clin Epidemiol&lt;/i&gt;. 1999;52(7):625-629. doi:10.1016/s0895-4356(99)00047-5

&lt;sup&gt;1&lt;/sup&gt;University of Missouri-Columbia, Columbia, MO, US, fares.alahdab@health.missouri.edu; &lt;sup&gt;2&lt;/sup&gt;University of Texas Health Science Center, Houston, TX, US; &lt;sup&gt;3&lt;/sup&gt;BMJ Evidence-Based Medicine, London, UK; &lt;sup&gt;4&lt;/sup&gt;&lt;i&gt;BMJ&lt;/i&gt;, London, UK.

&lt;h4&gt;Conflict of Interest Disclosures&lt;/h4&gt;
&lt;p&gt; None reported.&lt;/p&gt;


The 10th Peer Review Congress

Quality and Comprehensiveness of Peer Reviews of Journal Submissions Produced by Large Language Models vs Humans

artificial intelligence and large language models

editorial and peer review process

peer review

The Tenth Peer Review Congress featured 3 days for presentations of new research into peer review and all aspects of scientific publication, bias, quality of reporting, and information access and dissemination.

This year’s meeting was hybrid, and all plenary sessions were livestreamed with opportunities to view presentations and engage with participants in person and virtually during and after the meeting. All recordings are now available on Underline for viewing.

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#### Why? 
Our aim is to encourage research into the quality and credibility of peer review and scientific publication, to establish the evidence base on which scientists can improve the conduct, reporting, and dissemination of scientific research.

#### Who? 
Participants include editors and publishers of scientific peer-reviewed journals, researchers, funders, bibliometric and informatics experts, information innovators, librarians, journalists, policymakers, ethicists, scientific information disseminators, and anyone interested in the progress of the scientific information enterprise and the quality of scientific evidence.

The Congress embraces a wide range of disciplines, including biomedicine, health and life sciences, applied sciences, basic sciences, physical and chemical sciences, mathematics, computer sciences, engineering, economics, and social sciences as well as new and emerging disciplines.

#### What? 
The Congress provides a forum for the presentation and discussion of new research into the quality and credibility of editorial and funding peer review, scientific and scholarly publication, research methods and reporting practices, identification and management of biases, transparency, reproducibility, access, publication and dissemination metrics, identification and management of threats to the integrity of science, and advances in information exchange.

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The Tenth Peer Review Congress features 3 days for presentations of new research into peer review and all aspects of scientific publication, bias, quality of reporting, and information access and dissemination.

Fares Alahdab,<sup>1,2,3</sup> Juan Franco,<sup>3,4</sup> Helen Macdonald,<sup>4</sup> Sara Schroter<sup>4</sup>
<h4>Objective</h4>
Peer reviewer fatigue is on the rise, as reviewers are overburdened with increasing tasks and manuscripts to review, making it challenging for journal editors to secure a sufficient number of high-quality reviews.<sup>1</sup> Despite the potential benefits of using large language models (LLMs) in editorial and peer review processes<sup>2</sup> and the potential to support reviewers with tasks, it is not yet known how good they are at peer review. Additionally, covert use of LLMs in peer review is increasingly suspected, with limited knowledge of positive or negative impact. We aim to compare the quality and comprehensiveness of peer reviews produced by 5 LLMs compared with peer reviewers for research submissions.
<h4>Design</h4>
A comparative study of peer reviews produced by LLMs (GPT4o, GPTo3, Claude 3.5, Gemini 1.5 Pro, and Gemini 2.0 Flash) compared with 2 human peer reviews for research submissions between May 2024 and June 2025. Manuscripts were uploaded to Google’s Vertex AI, a private and secure BMJ Group workspace for using LLMs; the same prompt was used for all submissions (single-shot prompting). An experienced editor performed the ratings of all LLM and human reviews using the Review Quality Instrument (RQI),<sup>3</sup> a tool for assessment of review quality comprising 8 questions, each on a 5-point Likert scale. Secondary outcomes include a comprehensiveness score, based on elements of the manuscript that the reviews focused on, and whether they were evaluative in nature. Additionally, once the first decision has been made on the manuscripts, we plan to invite authors to complete a survey about their perceptions of both the LLM review reports and the peer reviewer reports for their manuscripts. We also plan to have a second editor perform the ratings and compute interrater agreement. Reviews produced by LLMs are not included in the decision-making for the manuscripts. Wilcoxon rank sum tests were used to compare LLM and human scores for each RQI item.

<h4>Results</h4> Preliminary data include 35 reviews (25 by LLMs and 10 by peer reviewers) of 5 BMJ submissions. Across 8 RQI items, LLM-generated reviews had higher mean (SD) scores than human reviewers on identifying strengths and weaknesses (4.12 [0.53] vs 2.70 [0.95]; P &lt; 0.001); providing useful comments on the writing, organization, tables, and figures (3.72 [0.79] vs 1.80 [0.63]; P &lt; .001); and constructiveness (4.00 [0.58] vs 3.00 [1.05]; P = .004) (<b>Table 25-1146</b>). Complete data will include 200 submissions to 4 BMJ journals, the secondary outcomes, and authors’ feedback on the LLM reviews.

![](https://assets.underline.io/markdown_image/1/image/b23f8733c131796f4e73f037ea693a62.png)

<h4>Conclusions</h4>
LLM-generated reviews matched or exceeded human reviewers on a few key dimensions of review quality. A fuller analysis will shed more light on the potential value of LLM peer reviews and how they could complement human peer reviewers’ work.
<h4>References</h4>
1. Publons. Global State of Peer Review report. <a href="https://publons.com/community/gspr">https://publons.com/community/gspr</a>

2. Liang W, Zhang Y, Cao H, et al. Can large language models provide useful feedback on research papers? a large-scale empirical analysis. <i>arXiv. </i>Preprint posted October 2,<i> </i>2023. <a href="https://doi.org/10.48550/arXiv.2310.01783">doi.org/10.48550/arXiv.2310.01783</a>

3. van Rooyen S, Black N, Godlee F. Development of the review quality instrument (RQI) for assessing peer reviews of manuscripts.<i> J Clin Epidemiol</i>. 1999;52(7):625-629. doi:10.1016/s0895-4356(99)00047-5

<sup>1</sup>University of Missouri-Columbia, Columbia, MO, US, fares.alahdab@health.missouri.edu; <sup>2</sup>University of Texas Health Science Center, Houston, TX, US; <sup>3</sup>BMJ Evidence-Based Medicine, London, UK; <sup>4</sup><i>BMJ</i>, London, UK.

<h4>Conflict of Interest Disclosures</h4>
<p> None reported.</p>


technical paper

<p>Ana Marušić<sup>1</sup></p>
<h4>Importance </h4>
<p>Authorship is the currency of academia and research, but it is not only about recognition. It brings accountability and transparency and can have ethical and legal implications.</p>
<h4>Observations </h4>
<p>Starting from the call for contributors to replace authors by Drummond Rennie and his colleagues almost 30 years ago,<sup>1</sup> the evolution of scientific authorship and contributorship models will be presented. Current challenges to authorship will also be explored, from paper mills to artificial intelligence.</p>
<h4>Conclusions </h4>
<p>Authorship has not been replaced by contributorship and will need a systemic approach to balance scientific credit, career advancement, and ethical responsibility.</p>
<h4>Reference</h4>
<p>1. Rennie D, Yank V, Emanuel L. When authorship fails: a proposal to make contributors accountable. <i>JAMA</i>. 1997;278(7):579-585. doi:10.1001/jama.278.7.579</p>

<p><sup>1</sup>University of Split School of Medicine, Split, Croatia; ana.marusic@mefst.hr.</p>
<h4>Conflict of Interest Disclosures</h4> Ana Marušić is a member of the Peer Review Congress Advisory Board but was not involved in the review or decision for this abstract.


Forward to the Past - Making Contributors Accountable

<p>Isamme AlFayyad,<sup>1</sup> Maurice Zeegers,<sup>2</sup> Lex Bouter,<sup>3</sup> Helen Macdonald,<sup>4</sup> Sara Schroter<sup>4</sup></p>
<h4>Objective </h4>
<p>Recent surveys report that a high percentage (50%-76%) of researchers use artificial intelligence (AI) in their research.<sup>1,2</sup> In April 2024, BMJ Group mandated submitting authors to disclose and describe their use of AI.<sup>3</sup> We analyzed the frequency of self-disclosed use of AI in research manuscripts submitted to 49 BMJ Group biomedical journals and identified types of AI tools used and the tasks they assisted with. We also compared the characteristics of manuscripts disclosing AI use compared with those not disclosing it.</p>
<h4>Design </h4>
<p>A cross-sectional analysis of original research submitted between April and November 2024 was conducted and reported using the STROBE reporting guideline. Main study outcomes were frequency of self-disclosed use of AI, types of AI tools used, and tasks they assisted with. Characteristics of submissions (region of submitting author, number of authors, acceptance rate, impact factor, general medical/specialty journal, peer review model) were extracted from journals’ manuscript tracking systems and websites. χ² test was used to compare proportions of these characteristics between submissions disclosing and not disclosing AI use. Factors associated with disclosing AI use were assessed using binomial logistic regression. A <i>P</i> value threshold for significance was set at .05.</p>
<h4>Results </h4>
<p>There were 25,114 eligible submissions from Asia (13,505 [53.8%]), Europe (6523 [26.0%]), North America (2795 [11.1%]), Africa (1196 [4.8%]), Oceania (708 [2.8%]), and South America (387 [1.5%]). A total of 1431 of 25,114 submissions (5.7%) disclosed AI use. The most common types of AI tools used were generative AI chatbots (812/1431 [56.7%]), writing assistant tools (182 [12.7%]), and visual/image processing tools (38 [2.7%]) (<b>Table 25-1013</b>). The majority of submitting authors who disclosed AI use reported using it to improve the quality of their writing (1248/1431 [87%]), and 28% (n = 399) reported using it for other purposes, including translation (107 [7.5%]), analyzing or collecting data (44 [3.1%]), generating data and output (87 [6.1%]), code writing (15 [1.0%]), image processing (36 [2.5%]), literature searches (49 [3.4%]), and managing references (8 [0.6%]). The percentage of submissions with disclosed AI use varied significantly by region (highest in Europe: 31.0%; lowest in Oceania: 1.0%; <i>P</i> &lt; .001). Submissions disclosing AI use had a lower mean number of authors (8.39 vs 8.90; <i>P</i> = .005). Authors from Europe (odds ratio [OR], 3.61 [95% CI, 2.11-6.18]), North America (OR, 2.30 [95% CI, 1.32-4.02]), South America (OR, 4.93 [95% CI, 2.62-9.28]), Asia (OR, 2.87 [95% CI, 1.68-4.89]), and Africa (OR, 3.34 [95% CI, 1.92-6.08]) were significantly more likely to disclose AI use than those from Oceania (<i>P</i> &lt; .05 for all). Conversely, each additional author reduced disclosure odds by 1% (OR, 0.99 [95% CI, 0.97-0.99]). Other characteristics of the journal (acceptance rate, impact factor, general medical/specialty journal, and peer review model) were not associated with AI use disclosure.</p>

![](https://assets.underline.io/markdown_image/1/image/4350638ef812c7db40bddf7b0b71c93c.png)

<h4>Conclusions </h4>
<p>The percentage of submitted articles with self-disclosed AI use was significantly lower in this study than what has been reported in recent surveys of researchers about their general use. Submitting authors may be underreporting their use of AI. </p>
<h4>References</h4>
<p>1. How are researchers responding to AI? <span class="text-roman">Oxford University Press. </span>May 23, 2024. Accessed September 1, 2024. <a href="https://corp.oup.com/news/how-are-researchers-responding-to-ai/">https://corp.oup.com/news/how-are-researchers-responding-to-ai/</a></p>
<p>2. <span class="text-roman">Insights 2024: Attitudes Toward AI.</span> Elsevier. 2024. Accessed September 1, 2024. <a href="https://www.elsevier.com/en-gb/insights/attitudes-toward-ai?utm_source=scalene-peer-review.beehiiv.com&amp;utm_medium=newsletter&amp;utm_campaign=scalene-8-chatgpt-as-reviewer-peer-structured-reviews">https://www.elsevier.com/en-gb/insights/attitudes-toward-ai</a></p>
<p>3. Macdonald H, Abbasi K. Riding the whirlwind: <i>BMJ</i>’s policy on artificial intelligence in scientific publishing. <i>BMJ</i>. 2023;382:1923. doi:10.1136/bmj.p1923</p>
<p><sup>1</sup>Research Center, King Fahad Medical City, Saudi Arabia, ialfayyad@gmail.com; <sup>2</sup>Department of Epidemiology, Care and Public Health Research Institute, Maastricht University, the Netherlands; <sup>3</sup>Department of Epidemiology and Data Science, Amsterdam University Medical Centers, Department of Philosophy, Faculty of Humanities, Vrije Universiteit Amsterdam, the Netherlands; <sup>4</sup><i>BMJ</i>, London, UK. </p>
<h4>Conflict of Interest Disclosures</h4> This research is part of an ongoing PhD collaboration between The BMJ and the team at Meta-Research at Maastricht University (UM) on the responsible conduct of publishing scientific research. The BMJ is published by BMJ Group, a wholly owned subsidiary of the British Medical Association. UM is a public legal entity in the Netherlands. This study is part of Isamme AlFayyad’s self-funded BMJ/UM PhD. No exchange of funds has taken place for this research project. All authors express their own opinions and not necessarily that of their employers. Helen Macdonald and Sara Schroter are full-time employees at BMJ Publishing Group. No other disclosures were reported. 
<h4>Additional Information</h4> The protocol of this research study was registered in Open Science Framework (osf.io/uvqms).



Authors Self-Disclosed Use of Artificial Intelligence in Research Submissions to 49 BMJ Group Biomedical Journals

<p>Roy H. Perlis,<sup>1,2</sup> Annette Flanagin,<sup>3</sup> Jacob Kendall-Taylor,<sup>3</sup> Michael Berkwits,<sup>4</sup> Kirsten Bibbins-Domingo<sup>3</sup></p>
<h4>Objective </h4>
<p>The scientific community is divided about the appropriate role of artificial intelligence (AI) in manuscript preparation.<sup>1</sup> In 2023, JAMA Network journals began requiring authors and reviewers to answer questions about use of AI to create or assist in the creation or editing of submitted manuscripts or with preparation of reviews.<sup>2</sup> This study was conducted to assess author and reviewer declarations of such use.</p>
<h4>Design </h4>
<p>Study of manuscripts and peer reviews submitted to 13 JAMA Network journals from September 2023 to May 2025. Numbers and proportions of manuscripts with corresponding authors and peer reviewers declaring use of AI were examined, along with factors associated with use (eg, journal, country, article type). For authors’ use, reasons, type of AI, and editorial decisions were also assessed; for reviewers’ use, turnaround time and ratings of reviews were assessed. Where sample sizes were sufficient, logistic regression models were run, with use of AI as the dependent variable and article type, journal, and review outcome as independent variables. A 2-sided uncorrected <i>P</i> value of &lt;.05 was considered the threshold for statistical significance. This study was deemed exempt from ethical approval by the Mass General Brigham Institutional Review Board.</p>
<h4>Results </h4>
<p>Of 82,829 manuscripts submitted during the study period, 2257 (2.7%) declared AI use. Authors’ use of AI increased from 1.6% in September 2023 to 4.2% in May 2025 (<i>r</i><sup>2</sup> = .34; <i>P</i> &lt; .01). <b>Table 25-1115</b> contrasts manuscripts with or without disclosed AI use by author country, journal, manuscript type, study type, editorial decision, AI use categories, and common AI models used. Author AI use was highest in <i>JAMA Network Open</i> (4.1%) and <i>JAMA</i> (3.1%) and lowest in <i>JAMA Dermatology</i> (1.6%) and was highest among authors from Taiwan (7.2%). Letters to the Editor (odds ratio [OR] 1.63, 95% CI 1.37-1.94) and Viewpoints (OR 1.44, 95% CI 1.21-1.72) were more likely to involve AI use than Original Investigations. The most common reported reasons for AI use by authors were for language and editing (50.0%), data analysis and statistics (11.8%), and content generation and drafting (8.2%). ChatGPT was the most common AI model used by authors (62.9%). Mean (SD) reviewer turnaround times were 11.8 (8.5) days for those who did not disclose AI use and 12.6 (7.6) days for reviewers who disclosed AI use. Reviewer use of AI was highest for <i>JAMA Network Open</i> (0.9%) and <i>JAMA Psychiatry</i> (0.8%) and among reviewers from the UK (2.7%).</p>

![](https://assets.underline.io/markdown_image/1/image/549162fd8a7ceeca532967bc4026042c.png)

<h4>Conclusions </h4>
<p>Among manuscripts submitted to 13 major medical journals, author declaration of AI use to create or edit content was low but more than doubled during the study period and varied by journal and article type. Very few reviewers disclosed use of AI, possibly because of guidance forbidding such use.<sup>3</sup> Limitations include small numbers of reported AI use, which limited comparisons for some factors, and the likelihood of underreporting of AI use.</p>
<h4>References</h4>
<p>1. Kwon D. Is it OK for AI to write science papers? <i>Nature</i> survey shows researchers are split. <i>Nature</i>. 2025;641:574-578. doi:10.1038/d41586-025-01463-8</p>
<p>2. Flanagin A, Pirracchio R, Khera R, Berkwits M, Hswen Y, Bibbins-Domingo K. Reporting use of AI in research and scholarly publication—JAMA Network guidance. <i>JAMA.</i> 2024;331(13):1096-1098. doi:10.1001/jama.2024.3471</p>
<p>3. Flanagin A, Kendall-Taylor J, Bibbins-Domingo K. Guidance for authors, peer reviewers, and editors on use of AI, language models, and chatbots. <i>JAMA</i>. 2023;330(8):702-703. doi:10.1001/jama.2023.12500</p>
<sup>1</sup>JAMA+ AI and <i>JAMA Network Open</i>, Chicago, IL, rperlis@mgh. harvard.edu; <sup>2</sup>Department of Psychiatry, Mass General Brigham, Boston, MA, US; <sup>3</sup><em>JAMA</em> and the JAMA Network, Chicago, IL, US; <sup>4</sup>Centers for Disease Control and Prevention, Atlanta, GA, US. 
<h4>Conflict of Interest Disclosures</h4> Roy H. Perlis receives payment for service as a scientific advisor to Circular Genomics, Genomind,and Alkermes. Annette Flanagin, Michael Berkwits, and Kirsten Bibbins-Domingo are members of the Peer Review Congress Advisory Board but were not involved in the review or decision for this abstract. 
<h4>Additional Information</h4>Llama 3.3 (Ollama) was used on June 13, 2025, to identify categories of author-declared use of artificial intelligence. Roy H. Perlis takes responsibility for the integrity of the content generated. 


Factors Associated With Author and Reviewer Declared Use of AI in Medical Journals

<p>Daniel S. Evanko,<sup>1</sup> Michael Di Natale<sup>1</sup></p>
<h4>Objective</h4>
<p> This study assessed the ability to reliably detect the use of generative artificial intelligence (genAI) by authors and reviewers in the field of cancer research, quantified this usage over time, and measured the impact of a change in policy on use of genAI by reviewers.</p>
<h4>Design</h4>
<p> In this cross-sectional study, original research manuscripts and reviewer comments submitted to 10 journals of the American Association for Cancer Research from 2021 through 2024 were analyzed for the presence of AI-generated text. The abstract, methods section, and reviewer comments were extracted from the submission system along with author and reviewer metadata. These sections were chosen because (1) the plain text was stored in the submission system, thus eliminating the need for extraction from files, and (2) we hypothesized that authors would most likely use genAI in the abstract and had observed that methods can be prone to false positives. Text was analyzed using an AI detection tool and classified by average AI likelihood score. Discrimination between full de novo generation of text, language translation, and editing was not possible.</p>
<h4>Results</h4>
<p> We analyzed 46,500 abstracts, 46,021 methods sections, and 29,544 reviewer comments. In 18,467 manuscripts submitted from 2021 through Q3 2022, only 7 abstracts—and no methods or reviewer comment text—were scored as highly likely to contain AI-generated text (AI likelihood score, 0.9 to 1). There were very few false positives (abstract: 0.22%, 40/18,467; methods: 0.07%, 13/18,303; reviewer comments: 0.03%, 4/12,908). Detection of AI-generated text in all 3 sources started increasing dramatically beginning in Q1 2023 after the public release of ChatGPT in November 2022 (<b>Figure 25-1180</b>). Detections in reviewer comments dropped by &gt;50% (from 46 to 34) in Q4 2023 after the journals implemented a policy prohibiting use of genAI for peer review before resuming a linear upward trend. In aggregate, the presence of AI-generated text in 2024 varied greatly between abstracts (23%, 2749/11,959), methods text (10%, 1209/11,875), and reviewer comments (4.8%, 348/7211). There was also substantial journal-dependent variation, from 12% to 15% of abstracts at 3 journals (183/1464, 29/214, 32/214) to 28% to 33% of abstracts at 2 journals (203/716, 438/1342). Authors affiliated with institutions in English-speaking countries were less than half as likely to use genAI compared with other authors. The presence of AI-generated text was associated with a substantially higher rejection rate before peer review regardless of institution location. There was a slight association of AI-generated text with lower-rated reviewer comments. </p>

![](https://assets.underline.io/markdown_image/1/image/0a82456056f57c54deb1f9fd0a3d35b4.png)

<h4>Conclusions</h4>
<p> AI-generated text can be detected in manuscripts and reviewer comments with virtually no false positives. GenAI usage is increasing rapidly, but journal policies to regulate usage can have a limited impact. Usage varies considerably between journals and the type of text and appears to be associated with different editorial outcomes.</p>
<sup>1</sup>American Association for Cancer Research, Philadelphia, PA, US, daniel.evanko@aacr.org. 
<h4>Conflict of Interest Disclosures</h4>
Daniel S. Evanko is a member of the Peer Review Congress Advisory Board but was not involved in the review or decision for this abstract. 
<h4>Acknowledgment</h4> 
We thank John Ho for his assistance collecting the data and entering it into the AI detection tool to obtain the AI likelihood scores.


Quantifying and Assessing the Use of Generative AI by Authors and Reviewers in the Cancer Research Field

<p>Ana-Catarina Pinho-Gomes,<sup>1,2</sup> Carinna Hockham,<sup>1</sup> Mark Woodward<sup>1,3</sup></p>
<h4>Objective</h4>
<p> Women are underrepresented among authors of scientific papers. Although the number of retractions has been rising over the past few decades,<sup>1</sup> gender differences among authors of retracted papers remain poorly understood. Therefore, this study investigated gender differences in authorship of retracted articles available on RetractionWatch.</p>
<h4>Design</h4>
<p> RetractionWatch (<a href="http://retractiondatabase.org/">http://retractiondatabase.org/</a>) provided us a dataset comprising 64,658 articles published between January 1971 and June 2025. This is an update of a previous study.<sup>2</sup> No articles were excluded. We extracted data for the first and last author of each paper and inferred their gender based on their first names using Gender-API software. We accepted gender predictions when the accuracy was estimated to be at least 80%. We estimated the percentage of retractions that could be attributed to women and men first authors and last authors overall and by reason. We used binomial distributions to estimate percentages retracted by women authors, with 95% CIs, and tested whether these percentages were significantly different from 50%. We compared women’s representation as authors of retractions vs their representation as authors of articles overall (40% for first authors and 30% for last authors<sup>3</sup>).</p>
<h4>Results</h4>
<p> Among 64,658 articles retracted between 1971 and 2025 that included 64,659 first authors and last authors, women accounted for 26.4% (95% CI, 25.8%-26.9%) of first authors and 23.7% (95% CI, 23.2%-24.2%) of last authors. There were significant differences in gender of authors in retracted articles by reason, and women’s representation appeared to be lower than their representation as authors of scientific articles (<span class="CharOverride-4"><b>Figure 25-0862</b></span>). The lowest representation of women was found for ethics and legal issues (16.6% [95% CI, 14.2%-19.2%] for first authors and 19.4% [95% CI, 17.1%-21.8%] for last authors). For first authors, women’s representation was the highest for errors caused by editors and publishers (33.8% [95% CI, 31.4%-36.4%]). For last authors, the highest representation of women was for plagiarism (28.9% [95% CI, 27.4%-30.6%]). Most retractions (60.9%) had men as first and last authors.</p>

![](https://assets.underline.io/markdown_image/1/image/6e7f48f38cfc6dda1a863b051bc8705f.png)

<h4>Conclusions</h4>
<p> Women’s representation among authors of retracted articles seemed to be slightly lower than women’s representation as authors of scientific papers overall. Women’s underrepresentation as authors of retractions due to fraud and misconduct as well as ethical concerns suggests that gender equality might enhance research integrity and, hence, reduce the negative impact that retractions have on population health and trust in science.</p>
<h4>References</h4>
<p>1. Gaudino M, Robinson NB, Audisio K, et al. Trends and characteristics of retracted articles in the biomedical literature, 1971 to 2020. <i>JAMA Intern Med. </i>2021;181(8):1118-1121. doi:10.1001/jamainternmed.2021.1807</p>
<p>2. Pinho-Gomes A-C, Hockham C, Woodward M. Women’s representation as authors of retracted papers in the biomedical sciences. <i>PLoS ONE</i>. 2023;18(5):e0284403. doi:10.1371/journal.pone.0284403</p>
<p>3. Squazzoni F, Bravo G, Farjam M, et al. Peer review and gender bias: a study on 145 scholarly journals. <i>Science Advances</i>. 2021;7(2):eabd0299. doi:10.1126/sciadv.abd0299</p>
<h4>Affiliations</h4> <sup>1</sup>The George Institute for Global Health, Imperial College London, London, UK, a.pinho-gomes@imperial.ac.uk; <sup>2</sup>Institute for Global Health, University College London, London, UK; <sup>3</sup>The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia. <h4>Conflict of Interest Disclosures</h4> Mark Woodward has been a consultant for Amgen and Freeline. No other disclosures were reported. <h4>Funding/Support</h4> Ana-Catarina Pinho-Gomes is funded by an Academic Clinical Lectureship by the National Institute for Health and Care Research (NIHR). Mark Woodward is supported by the National Health and Medical Research Council of Australia grant APP1174120. <h4>Role of Funder/Sponsor</h4> The funders had no influence in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the abstract; and decision to submit the abstract for presentation. <h4>Disclaimer</h4> The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.



Comparison of Reasons for Retraction of Biomedical Articles by Women and Men Authors

<p>Tim Kersjes<sup>1</sup></p>
<h4>Objective </h4>
<p>The rise of paper mills presents a significant problem for academic publishers. It is suspected that paper mill actors not only pollute the literature by publishing low-quality or even fake articles; they also try and enhance the credibility of their product by citing their own paper mill articles. This reinforces their credibility and attractiveness for paying customers. Papers mills also attempt to subvert the peer review process by suggesting their own actors as peer reviewers. This study is an analysis of how one successful paper mill used journals that rely on author-suggested reviewers to increase their output and credibility through the creation and use of fake authors and reviewers. The objective was to analyze how these fake personas amplified this paper mill’s success.</p>
<h4>Design </h4>
<p>Following a research integrity investigation by publisher Springer Nature, 55 articles in 4 of their journals (<i>Boundary Value Problems</i>, <i>Journal of Inequalities and Applications</i>, <i>Fixed Point Theory and Applications</i>, and <i>Advances in Difference Equations</i>) were identified to be compromised by a single paper mill, probably operating out of China. These articles were published between September 2013 and August 2020 in journals that asked submitting authors to suggest reviewers. In total, 26 fake personas were identified by confirming with their listed affiliations that these authors were in fact not with these institutions. Network mapping identified the relations between the fake personas and how these aided their publications, as well as citation patterns between articles authored and reviewed by these fake personas.</p>
<h4>Results </h4>
<p>The analysis demonstrated that this paper mill successfully introduced 2 fake personas early on by managing to get these personas as co-authors on published articles. This allowed the paper mill to submit an increasing number of articles (starting with 2 submissions in 2013 to a maximum of 12 submissions in 2016) with new fake personas, with previously published fake personas as suggested reviewers. Because these fake personas infiltrated the peer review process, more fake authors ended up in the published literature, allowing the paper mill to increase its publication output. Of the 26 fake personas, 13 managed to end up as published authors. For these 55 articles, 13 fake personas that are published authors were invited to review for a total of 68 times. In contrast, 13 fake personas without a publication record were only invited 25 times in total. Additionally, 48 of the 55 paper mill articles published by these fake personas cited 2 or more articles by other fake personas in an apparent attempt to further build credibility.</p>
<h4>Conclusions </h4>
<p>Fake personas can serve as both authors and reviewers. Once the paper mill has established a fake persona as credible, it can be used to amplify the credibility of additional fake personas, allowing a paper mill to build a network of fake authors and reviewers to subvert the peer review process. Identity verification and citation analysis may prevent paper mills from gaining success in this way.</p>
<sup>1</sup>Springer Nature, Dordrecht, the Netherlands, tim.kersjes@ springernature.com. 
<h4>Conflict of Interest Disclosures</h4> 
Tim Kersjes is a full-time employee of Springer Nature and owns Springer Nature stock. He also serves on the trustee board of the Committee on Publication Ethics.


Paper Mill Use of Fake Personas to Manipulate the Peer Review Process

<p>Coromoto Power Febres,<sup>1</sup> Julia Gunn,<sup>1</sup> Laura Wilson<sup>1</sup></p>
<h4>Objective </h4>
<p>Publishers, industry bodies, and previous literature have identified authorship changes as a potential means of detecting systematic manipulation activity in submissions to academic journals.<sup>1</sup> There is limited empirical evidence on the features associated with systematic manipulation<sup>2</sup> and, consequently, on their accuracy as indicators. This study tested the hypothesis that attempts to change article authorship post submission indicate further concerns.</p>
<h4>Design </h4>
<p>Since March 2023, all requests across 1321 Taylor &amp; Francis journals to change 3 or more authors following initial manuscript submission have been investigated by the Publication Ethics and Integrity team. This threshold was set based on previous experience that 3 or more changes after review was frequently associated with concerning authorship behavior. The request and associated articles were assessed, including adherence to editorial policies and indicators of systematic manipulation.<sup>3</sup> This cross-sectional study analyzed all such assessments conducted in 2024, consisting of 496 articles; 44 articles were excluded due to incomplete assessment. Requests for fewer than 3 changes are assessed by the journal’s academic editors, for which data are unavailable. The outcomes measured were the authorship change decision recorded and whether other concerns were noted during assessment. Adherence to editorial policies (eg, ethics and consent declarations, conflict of interest disclosure) and indicators of systematic manipulation (eg, tortured phrases, peer review manipulation) were also recorded; these 2 categories were combined into concerns noted<i> </i>and classified as <i>yes</i> or <i>no</i>. Odds ratio (OR) analysis tested associations between denied authorship changes and noted concerns. STROBE guidelines informed the study.</p>
<h4>Results </h4>
<p>Of the 452 articles assessed, most requests for 3 or more authorship changes were denied; 367 (81.2%) were denied vs 85 (18.8%) approved. For the 170 articles for which the data were available, the median (range) number of authors was 4 (3-10). A total of 301 requests (66.6%) had concerns and 151 (33.41%) had no concerns. Of the denied requests for authors changes, 292 (79.6%) had concerns and 75 (20.44%) had no concerns. Of approved requests, 76 (89.41%) had no concerns and 9 (10.59%) had concerns. Denied requests were more likely to have concerns (OR, 32.9 [95% CI, 15.9-68.6]) compared with approved requests. The latest decisions per paper per category are shown in <span class="CharOverride-4"><b>Table 25-1075</b></span>.</p>

![](https://assets.underline.io/markdown_image/1/image/33937be29af5a55dad1ad8e8e63d1896.png)

<h4>Conclusions </h4>
<p>This study suggests that irregularities in authorship changes may indicate research integrity concerns. Limitations include the relatively small sample size and assessment variability across team members/individual assessors. Further analysis is required to establish whether authorship change requests correlate with other systematic manipulation indicators. Further research could also involve analysis by discipline and testing for an association between author demographics and other characteristics, such as co-authorship history and acceptance rate, and a comparison with requests to change 2 or fewer names.</p>
<h4>References</h4>
<p>1.<span class="CharOverride-4"> </span>Abalkina A. Publication and collaboration anomalies in academic papers originating from a paper mill: evidence from a Russia-based paper mill. <i>Learned Publishing. </i>2023;36:689-702. doi:10.1002/leap.1574</p>
<p>2. Byrne JA, Abalkina A, Akinduro-Aje O, et al. A call for research to address the threat of paper mills. <i>PLoS Biol</i>. 2024;22(11). doi:10.1371/journal.pbio.3002931</p>
<p>3. Parker L, Boughton S, Bero L, Byrne JA. Paper mill challenges: past, present, and future. <i>J Clin Epidemiol</i>. 2024;176. doi:10.1016/j.jclinepi.2024.111549</p>
<sup>1</sup>Taylor & Francis Group, Beeston, UK, coro.powerfebres@tandf. co.uk. 
<h4>Conflict of Interest Disclosures</h4> 
Coromoto Power Febres reported being a full-time employee of Taylor & Francis and being a member of the STM Research Integrity Committee and United 2 Act Education and Awareness working group. Julia Gunn reported being a full-time employee of Taylor & Francis. Laura Wilson reported being a full-time employee of Taylor & Francis and being a member of the STM Membership committee, United 2 Act Trust markers working group, and ALPSP Policy committee.


Authorship Changes as an Indicator of Research Integrity Concerns in Submissions to Academic Journals

<p>Nicholas J. DeVito,<sup>1</sup> Christine Cunningham,<sup>1</sup> Ben Goldacre<sup>1</sup></p>
<h4>Objective </h4>
<p>Continued citation of retracted articles is an ongoing issue.<sup>1</sup> RetractoBot examines whether informing authors that they cited a retracted paper reduces future citations to those articles.<sup>2</sup></p>
<h4>Design </h4>
<p>This was a parallel-group, 2-group, superiority trial. We identified and randomized retracted articles in PubMed published since 2000. The study was unblinded, but primary outcome ascertainment was objective, and randomization and allocation were fully and simultaneously computer automated. Authors with publicly available contact information were notified about citations of retracted articles in the intervention group; no notifications were sent about articles in the control group. The email included a voluntary survey question on knowledge of the retractions and invited additional voluntary feedback. Retraction, contact, and citation data were obtained from PubMed and Scopus. Follow-up started 3 months after the final intervention email was sent to allow in-press manuscripts that could not be affected by our intervention a chance to publish. The primary outcome was the difference in the rate of new citations of retracted articles between groups. We used a negative binomial model adjusted for years since retracted article publication, and our stratification variable, with outliers removed. Data analysis was performed in Stata version 16. We also report the results of a survey question on knowledge of retracted citations, and a sentiment analysis on email feedback. Additional methods details, including power calculations, are available in our prespecified protocol<sup>3</sup> and at retracted.net.</span></p>
<h4>Results </h4>
<p>We randomized 7958 retracted articles to the intervention group and 7963 to the control group (<b>Figure 25-0938</b>). Overall, 246,749 intervention emails were deliverable to authors between January 8 and February 7, 2024, with follow-up extending from May 8, 2024, to May 7, 2025. After removing outliers, there were 7939 citations of articles in the intervention group and 7943 in the control group. The effect of our intervention was not significant (mean citation rate, −0.007; 95% CI, −0.055 to 0.041) and held for all prespecified sensitivity analyses. We received 15,667 survey responses from notified authors with 80.6% (12,631) indicating they were unaware of all retracted citations we informed them about. We received 482 substantive pieces of email feedback, of which 57.7% (278) indicated a positive sentiment about the project (vs neutral, negative, mixed, or other).</p>

![](https://assets.underline.io/markdown_image/1/image/63dcfd9d05b6055115861cb2b4f70ce9.png)

<h4>Conclusions </h4>
<p>Our intervention did not significantly decrease citations of retracted articles at 1 year compared with the control group. We plan future secondary analyses to determine whether an effect emerges with increased follow-up. Of the authors who responded, most were unaware of the notified retractions and had a positive view of the intervention, indicating the potential for future interventions in this area. The continued uptake of retraction notifications in citation management software, using sources such as the RetractionWatch database, may already be having an effect in this space. </p>
<h4>References</h4>
<p>1. Kühberger A, Streit D, Scherndl T. Self-correction in science: the effect of retraction on the frequency of citations. <i>PLoS One</i>. 2022;17(12):e0277814.</p>
<p>2. Xu H, Ding Y, Zhang C, Tan BCY. Too official to be effective: an empirical examination of unofficial information channel and continued use of retracted articles. <i>Res Policy</i>. 2023;52(7):104815.</p>
<p>3. Cunningham C, DeVito N, Goldacre B. RetractoBot—revised stage 1 registered report protocol. Published online October 31, 2023. doi:10.6084/M9.FIGSHARE.24468391.V1</p>
<p><sup>1</sup>Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK, nicholas.devito@phc.ox.ac.uk.</p>
<h4>Conflict of Interest Disclosures</h4> 
Nicholas J. DeVito reported receiving funding from the Naji Foundation, the Fetzer-Franklin Memorial Fund, the European Commission, and UK Research and Innovation (UKRI) outside the submitted work. Ben Goldacre reported receiving research funding from the Bennett Foundation, the Laura and John Arnold Foundation, the National Health Service (NHS) National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Society, Health Data Research UK, the Health Foundation, the World Health Organization, UKRI Medical Research Council, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies program outside the submitted work; he has previously been a non-executive director at NHS Digital; he also receives personal income from speaking and writing for lay audiences on the misuse of science. 
<h4>Funding/Support</h4>The RetractoBot project was funded by a grant from the Laura and John Arnold Foundation and a grant and in-kind support from Elsevier. 
<h4>Role of Funder/Sponsor</h4> The funders had no role in the study design, data collection (outside of in-kind access to elements of the Scopus API from Elsevier), data analysis, interpretation, decision to submit, or any other facet of the planning, conduct, or dissemination of the presented work. 
<h4>Acknowledgment</h4> We would like to acknowledge the full RetractoBot study team including Karolina Wartolowska, Henry Drysdale, Benjamin Feakins, Helen J. Curtis, Francis Irving, Anna Powell-Smith, and Seb Bacon.


Notifying Authors That They Have Cited a Retracted Article and Future Citations of Retracted Articles: The RetractoBot Randomized Controlled Trial

<p>Leslie D. McIntosh,<sup>1</sup><span lang="ar-SA"> Hélène Draux</span>,<sup>1</sup> Elizabeth Smee,<sup>1</sup> Cynthia Hudson Vitale<sup>2</sup></p>
<h4>Objective </h4>
<p>To understand the broad impact of one questionable network on publishing, including the publishers, countries, and institutions involved and affected by the network’s activity.</p>
<h4>Design </h4>
<p>We examined the Pharmakon Neuroscience Research Network (PNN), a questionable entity listed in funding statements, acknowledgments, and author affiliations in articles published between 2019 and 2023. We selected the PNN because it was the first potential broad-scale case of scientific manipulation identified by one of the authors (L.D.M), and analyzing the PNN has helped the development of patterns to discover other unusual collaboration networks. All documents containing “Pharmakon Neuroscience” anywhere in the title, abstract, author affiliations, and full text were selected using Dimensions.<sup>1</sup> We excluded retraction and correction notices; articles for which a peer reviewer identified their organization as PNN; and articles with more than 25 authors. We inspected the metadata associated with the articles, notably the organizations related to the articles. We situated these practices within the Taxonomy of Scientific Manipulation,<sup>2</sup> systematically identifying (1) who was involved (authors, institutions, and publishers), (2) where<span class="CharOverride-9"> </span>mechanisms were used to manipulate science (journals, countries, and institutions), and (3) how the science was manipulated (methods for deceiving scholarly communication).</p>
<h4>Results </h4>
<p>Pharmakon Neuroscience was referenced in 140 published articles as of January 2025. After applying exclusion criteria, 123 articles remained. These articles included more than 6000 citations (citation ratio of 51 citations per article). The articles had 361 unique authors, including 29 without author identifiers (eg, ORCiD, Dimensions). The PNN involved 56 journals across 12 publishers, with Springer Nature (n = 29), Elsevier (n = 26), and Bentham Science Publishers (n = 25) having the most articles involved out of the total (n = 123). The involved authors were affiliated with 40 countries and 232 organizations. Of these organizations, 212 had research identifiers (eg, Research Organization Registry, Global Research Identifier Database), and 20 did not. Three of the organizations appeared to be personal homes, even though they were claimed as businesses or educational institutions. Additionally, one publication<sup>3</sup> highlighted the PNN as an affiliation among the “top 25 most predictive and impactful authors.”</p>
<h4>Conclusions </h4>
<p>This investigation leveraged publication databases and advanced forensic scientometric techniques to highlight critical research integrity and security vulnerabilities in a questionable research network. We use the term “questionable network” because this co-authorship network appears to have grown too rapidly to have developed naturally from scholarly affiliations over time. The impact of the PNN’s work might be legitimized through future studies and become seen as normative behavior. Although the most prolific PNN author and organization stem from one country, there is a global impact not confined to high- or low-income countries. While one individual appears central to the network, a single orchestrator does not imply sole responsibility. Instead, this case underscores the complexity of manipulating the scientific ecosystem, where questionable actors exploit the scholarly communication system through nonverifiable funders and organizations and artificially inflated citation metrics.</p>
<h4>References</h4>
<p>1. Hook DW, Porter SJ, Herzog C. Dimensions: building context for search and evaluation. <i>Front Res Metr Anal</i>. 2018;3:23. doi:10.3389/frma.2018.00023</p>
<p>2. McIntosh LD, White W, Vitale CH. Unveiling deception: establishing a taxonomic framework for disinformation within scientific discourse. <i>arXiv</i>. Preprint posted online November 19, 2023. doi:10.48550/arxiv.2311.11344</p>
<p>3. Sab MC, Ahmed KM. Tracing Bangladesh’s scientific footprints: a scientometric expedition into inflammation pharmacology. <i>Inform Res Comm</i>. 2024;1(1):9-21. doi:10.5530/irc.1.1.4</p>
<sup>1</sup>Digital Science, London, UK, l.mcintosh@digital-science.com; <sup>2</sup>Johns Hopkins University, Baltimore, MD, US. 
<h4>Conflict of Interest Disclosures</h4> Leslie D. McIntosh, Hélène Draux, and Elizabeth Smee are employees of Digital Science, a research technology company.


How a Questionable Research Network Manipulated Scholarly Publishing

<p>Elena Álvarez-García,<sup>1</sup> Daniel Garcia-Costa,<sup>1</sup> Flaminio Squazzoni,<sup>2</sup> Mario Malički,<sup>3,4,5</sup> Bahar Mehmani,<sup>6</sup> Francisco Grimaldo<sup>1</sup></p>
<h4>Objective </h4>
<p>While the publication of review reports increases the transparency of peer review, little is known about the effect it has on the content or quality of reports.<sup>1</sup> The objective of our study was to assess differences in length, information content, and similarity of open vs unpublished reports. </p>
<h4>Design </h4>
<p>This was a cross-sectional study following STROBE guidelines for reporting<sup>2</sup> that compared open with unpublished reports from 233 medical journals from Elsevier and Springer Nature, submitted from 2016 to 2021. Reports were obtained through confidential agreements with the publishers. Content of reports was compared using number of sentences, previously validated models for classification of 8 categories of content,<sup>3</sup> information content score (mean cumulative distribution function values of each category’s sentence count, based on a zipfian distribution, with scores ranging from 0 to 1, where 1 indicates the highest informativeness), and information score dispersion measured by the Gini index (measure of similarity between reports for the same manuscript, where 0 indicates total similarity and 1 indicates total dissimilarity). Number of sentences, information score, and dispersion were compared using Mann-Whitney <i>U</i> tests. The association of information score and Gini index with peer review type, journal quartile of impact factor, and reviewer gender, seniority, and region were explored using generalized linear models. To adjust for multiple comparisons, we considered <i>P</i> &lt; .001 as statistically significant.</p>
<h4>Results</h4>
<p> Compared with unpublished reports (n = 117,250), open peer review reports (n = 40,844) were longer (median [IQR] length, 22 [12-37] sentences vs 17 [10-28] sentences; <i>P</i> &lt; .001) than unpublished reports and had a higher informative content (median [IQR] score, 0.52 [0.35-0.70] vs 0.46 [0.30-0.65]; <i>P</i> &lt; .001), with the largest difference found in the number of suggestion and solution sentences (Figure 25-1081</b>). Women’s reports had a higher information score than men’s reports (difference, 6.3%), and reviewers from non-Western institutions had lower scores than those from Western institutions (difference, −6.0%). Open peer review reports were also more similar to each other (median [IQR] Gini index, 0.19 [0.09-0.33] vs 0.22 [0.11-0.35]; <i>P</i> &lt; .001).</p>

![](https://assets.underline.io/markdown_image/1/image/6f7beaaf8f16b79237fbcb5ba46ff349.png)

<h4>Conclusions </h4>
<p>Our study showed that open peer review reports were longer than traditional unpublished reports, with the greatest differences found in the number of suggestion and solution sentences. These results suggest that increasing the transparency of peer review could lead to more detailed reports that focus on manuscript improvement. <br />A limitation of our study is that we did not have access to published manuscripts and were unable to determine the impact of initial quality of manuscripts beyond journal ranking, nor the impact peer review reports had on manuscript improvement.</p>
<h4>References</h4>
<p>1. Ross-Hellauer T, Horbach SP. Additional experiments required: a scoping review of recent evidence on key aspects of open peer review. <i>Res Eva</i>. Published online February 8, 2024. doi:10.1093/reseval/rvae004</p>
<p>2. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. <i>Int J Surg</i>. 2014;12(12):1495-1499. doi:10.1016/j.ijsu.2014.07.013</p>
<p>3. Severin A, Strinzel M, Egger M, et al. Relationship between journal impact factor and the thoroughness and helpfulness of peer reviews. <i>PLoS Biol</i>. 2023;21(8):e3002238. doi:10.1371/journal.pbio.3002238</p>
<sup>1</sup>Department of Computer Science, University of Valencia, Valencia, Spain; <sup>2</sup>Department of Social and Political Sciences, University of Milan, Milan, Italy; <sup>3</sup>Stanford Program on Research Rigor and Reproducibility, Stanford University, Stanford, CA, US, mmalicki@ stanford.edu; <sup>4</sup>Department of Epidemiology and Population Health, Stanford University, Stanford, CA, US; <sup>5</sup>Meta-Research Innovation Center at Stanford, Stanford University, Stanford, CA, US; <sup>6</sup>STM Journals, Elsevier, Amsterdam, the Netherlands. <h4>Conflict of Interest Disclosures</h4> Mario Malički was an Editor in Chief of Research Integrity and Peer Review, which is published by Springer Nature, provider of the part of the dataset. Bahar Mehmani is an employee of Elsevier, provider of the other part of the dataset. Bahar Mehmani is a member of the Peer Review Congress Advisory Board but was not involved in the review or decision for this abstract. No other disclosures were reported. <h4>Funding/Support</h4> Elena Álvarez-García, Daniel GarciaCosta, and Francisco Grimaldo have been partially supported by the Regional Ministry of Education, Culture, Universities and Employment of the Generalitat Valenciana under project CIAICO/2022/154. Flaminio Squazzoni was supported by a grant from the Progetti di Rilevante Interesse Nazionale—Italian Ministry of University and Research (grant 202297CKET_00 “ALGOLIT”). <h4>Role of the Funder/Sponsor</h4> The funder was not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the abstract; and decision to submit the abstract for presentation. <h4>Acknowledgment</h4> We gratefully acknowledge the support of Springer Nature, which provided the open peer review reports for our study, specifically Suzuki Limbu, Attilia Czikmantori, and their production team. We also thank all reviewers of Springer Nature journals that participated in signing or allowing their review reports to be published and all journals and editors facilitating these processes. We acknowledge the support on data extraction from the IT staff of Elsevier, specifically Ramsundhar Baskaravelu and his team. This work uses Scopus data provided by Elsevier through the Peer Review Workbench. We also thank Dave Santucci from the Elsevier Scopus API team and Kristy James from the International Center for the Study of Research for their support on data enrichment about authors and reviewers. We thank Josep Monclús for his help on data extraction and preparation during the initial phase of this study.



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