bims-skolko Biomed News
on Scholarly communication
Issue of 2026–03–01
thirty-two papers selected by
Thomas Krichel, Open Library Society
  1. Assessing the costs of academic engagement in plastic surgery in the United States.
  2. Pop-up journals for policy research: can temporary titles deliver answers?
  3. Favorable research environment is a key determinant of research integrity according to a ten-country survey across Central and Eastern Europe.
  4. Quantifying new threats to health and biomedical literature integrity from rapidly scaled publications and problematic research.
  5. Statement of Retraction: Assessing database accuracy for article retractions: A preliminary study comparing Retraction Watch Database, PubMed, and Web of Science.
  6. Responsible use of large language models in manuscript authorship, peer review, and editorial processes: a Delphi consensus among editors-in-chief of anaesthesia and pain medicine journals (RULE-AP).
  7. Academic journals' AI policies fail to curb the surge in AI-assisted academic writing.
  8. Detecting Reference Errors in Scientific Literature with Large Language Models.
  9. Peer review and the limits of artificial intelligence (Letter in response to "Peer review in the age of artificial intelligence: a comparative study of human and AI-generated review reports").
  10. Using AI to improve peer review and research integrity in scientific journals.
  11. This AI can improve your peer review - and make it more polite.
  12. Can ChatGPT effectively generate abstracts in orthopedic surgery? A comparative analysis between human-written and ChatGPT-generated scientific abstracts.
  13. How do authors want to use AI for review? : A survey to assess the perception of scientists who received both AI and human reviews of their manuscripts.
  14. Increasing Use of Large Language Models for Scholarly Writing in Radiology.
  15. Five ways to spot when a paper is a fraud.
  16. [The Interplay between Peer Review and the Quality and Reliability of Scientific Publications: Research Innovations].
  17. Screening, sorting, and the feedback cycles that imperil peer review.
  18. Putting the "Peer" Back in Peer Review.
  19. The Current State of Peer Review.
  20. Refocusing peer review on science rather than formatting requirements.
  21. The Contributor Role Taxonomy tool must serve to record extent of authorship.
  22. Holding the Scalpel: Scientific Authorship and Responsibility in the Era of Generative Artificial Intelligence.
  23. Approaching the Blank Document: Gamification of Manuscript Writing From a Trainee's Perspective.
  24. Artificial intelligence-assisted statistical analysis and statistical review: evidence (2023-2025) and implications for internal medicine.
  25. Number of Authors in Japanese Obstetrics and Gynecology Case Reports: A Preliminary Observation.
  26. Older Doctors and Continuing to Write: An Evaluation Perspective.
  27. Editor's Note on CONSORT-C and SPIRIT-C Guidelines.
  28. Authorship inflation in general practice research: unanswered questions to address?
  29. Standardization Catalyzes Scientific Innovation.
  30. Account for AI in the environmental footprint of scientific publishing.
  31. Behind the Doors: 24 Insights Into Scientific Publishing.
  32. PreprintToPaper dataset: connecting bioRxiv preprints with journal publications.
  1. J Plast Reconstr Aesthet Surg. 2025 Dec 18. pii: S1748-6815(25)00739-9. [Epub ahead of print]
    Assessing the costs of academic engagement in plastic surgery in the United States.
    Pushti Shah, Jacquelyn Roth, Sofia Aisiks, Edward Lee.
       BACKGROUND: Academic engagement plays a central role in education, leadership development, and career advancement in Plastic Surgery. While other surgical specialties have evaluated the financial barriers to such involvement, plastic surgery lacks a comprehensive cost analysis. This cross-sectional observational study seeks to quantify the cumulative costs associated with academic participation across societies, meetings, and publishing platforms.
    METHODS: Major plastic surgery societies, meetings, and journals were identified by two authors through author consensus and web-based searches. Cost data was collected in 2025 from official online sources or directly from society and meeting coordinators. A baseline academic engagement (BAE) cost was calculated for newly board-certified surgeons, and total lifetime costs were estimated assuming a 34-year clinical career. Descriptive statistics were used to summarise costs across categories and subspecialties.
    RESULTS: The average BAE was $19,367.32, with an annual continuation cost of $15,176.72. Over a 34-year career, the total estimated cost of academic engagement was $520,199.08. Median conference attendance cost was $876 for members, excluding travel and lost productivity. CME credit acquisition averaged $47.20 per credit for members and $55.71 for nonmembers. Journal subscriptions and open-access publication fees further added to the financial burden. Costs were highest in aesthetic and craniofacial subspecialties.
    CONCLUSIONS: The cumulative costs of academic participation, including society membership, conference attendance, and publication, may limit engagement, particularly for early-career, international, and private practice surgeons. Expanding virtual CME options, increasing scholarship availability, and reevaluating CME accreditation criteria may help reduce disparities and ensure broader, sustained involvement in academic plastic surgery.
    Keywords:  Academic; CME; Costs; Financing; Society membership
    DOI:  https://doi.org/10.1016/j.bjps.2025.12.011
  2. Nature. 2026 Feb 25.
    Pop-up journals for policy research: can temporary titles deliver answers?
    Dalmeet Singh Chawla.
      
    Keywords:  Economics; Government; Policy; Publishing
    DOI:  https://doi.org/10.1038/d41586-026-00446-7
  3. Sci Rep. 2026 Feb 23.
    Favorable research environment is a key determinant of research integrity according to a ten-country survey across Central and Eastern Europe.
    Alliance for Life Focus Group 2: Research Ethics and Research Integrity
      
    Keywords:  Biomedical research; Fabrication; Falsification; Plagiarism; Questionable research practices; Research environment; Research integrity; Scientific fraud; Scientific misconduct
    DOI:  https://doi.org/10.1038/s41598-026-39928-z
  4. J Clin Epidemiol. 2026 Feb 23. pii: S0895-4356(26)00078-8. [Epub ahead of print] 112203
    Quantifying new threats to health and biomedical literature integrity from rapidly scaled publications and problematic research.
    Matt Spick, Anthony Onoja, Charlie Harrison, Stefan Stender, Jennifer Byrne, Nophar Geifman.
       BACKGROUND AND OBJECTIVES: The last three years have seen an explosion in published manuscripts analysing open-access health datasets, in many cases presenting misleading or biologically implausible findings. There is a growing evidence base to suggest that this is due in part to AI-assisted and formulaic workflows, and publishers are responding by discouraging submissions employing open-access health datasets.
    METHODS: Here we employ a scientometric analysis to investigate which datasets have seen publication rates deviate from previous trends, especially where this coincides with changes to author geographical origins and increases in formulaic titles.
    RESULTS: Across 36 datasets we identify nine showing hallmarks of paper mill exploitation (FAERS, NHANES, UK Biobank, FinnGen, the Global Burden of Disease Study, MIMIC, CHARLS, CDC WONDER, and TriNetX). These nine datasets had, in 2025, a combined publication count of 23,005 indexed in the OpenAlex database. This represents an excess of 11,577 publications above the AutoRegressive Integrated Moving Average (ARIMA) forecast trend, and is a 3.0x fold change on the 7,655 publication count for these nine datasets in 2022. We also identified a notable difference in the fold change for China (4.2x) versus the rest of the world (1.9x) and an increase in formulaic titles.
    CONCLUSIONS: These findings highlight potential risks to research integrity in areas such as public health and drug safety, and especially to the accessibility and interoperability principles central to Open Science and FAIR data practices. We argue that permissive open-access data policies naturally facilitate exploitative workflows, and that these findings add to the case for the safeguarding mechanisms to preserve the goals of Open Science.
    DOI:  https://doi.org/10.1016/j.jclinepi.2026.112203
  5. Account Res. 2026 Feb 24. 1
    Statement of Retraction: Assessing database accuracy for article retractions: A preliminary study comparing Retraction Watch Database, PubMed, and Web of Science.
      
    DOI:  https://doi.org/10.1080/08989621.2026.2633865
  6. Br J Anaesth. 2026 Feb 25. pii: S0007-0912(26)00055-3. [Epub ahead of print]
    Responsible use of large language models in manuscript authorship, peer review, and editorial processes: a Delphi consensus among editors-in-chief of anaesthesia and pain medicine journals (RULE-AP).
    Alessandro De Cassai, Burhan Dost, John G Augoustides, Leonard Azamfirei, Zekeriyya Alanoğlu, Liana Maria Azi, José Andrés Calvache, Vladimir Cerny, Stefan De Hert, Abdelazeem Eldawlatly, Michaela K Farber, Diogo Sobreira-Fernandes, Michael R Fettiplace, Dario Galante, Rakesh Garg, Heidi V Goldstein, Alfredo Abad-Gurumeta, Lalit Gupta, Hugh C Hemmings, Christopher A Jones, Marc C Hochberg, Joel D Katz, Hyun Kang, Gül Köknel Talu, Durval C Kraychete, Ruth Landau, Sangseok Lee, Hillary D Lum, Christina Lundgren, Thamasi Rekha Makuloluwa, Paolo Martelletti, Tonya M Palermo, Philip J Peyton, Pierrick Poisbeau, James P Rathmell, Antoine Roquilly, Stephan K W Schwarz, Madhuragauri Shevade, Paul A Sloan, BobbieJean Sweitzer, Ary Serpa Neto, Philip F Stahel, Zerrin Özköse Şatırlar, Alparslan Turan, Dennis C Turk, Massimiliano Valeriani, Mads U Werner, Paul J Young, Igor Borisovich Zabolotskikh, Kai Zacharowski, Szymon Zdanowski.
      This article presents a Delphi consensus developed by a panel of editors-in-chief of anaesthesiology and pain medicine journals to guide the responsible use of large language models (LLMs) in academic publishing. LLMs offer potential benefits for scientific writing, including language editing, summarisation, translation, information organisation, and support for non-native English speakers, but their misuse raises concerns about accuracy, transparency, confidentiality, and research integrity. Through a three-round modified Delphi process involving 53 editors-in-chief or their delegates, 59 statements were generated and categorised into guidance for authors, editors, reviewers, and publishers with a particular attention to LLM disclosure practices and perceived risks. The consensus recognises that LLMs are useful tools in academic publishing for authors, reviewers, and editors. However, their use must be guided by ethics, legality, and principles of transparency and accountability. LLMs may assist with limited editorial and authorial tasks provided that their use is fully disclosed and all outputs are verified by humans. The consensus also emphasises the inappropriateness of using LLMs to generate original or ideative content, which should remain a strictly human responsibility. Moreover, LLMs must not generate data, references, conclusions, or entire manuscripts, nor be used for editorial decisions or peer-review reports. Editors expressed concerns about 'hallucinations', erosion of critical skills, confidentiality breaches, and the proliferation of low-quality LLM-generated manuscripts. The resulting guidance highlights transparency, human accountability, and careful verification as essential principles for integrating LLMs into scholarly workflows while preserving the integrity of scientific publishing.
    Keywords:  Delphi consensus; anaesthesia and pain medicine journals; editorial policy; large language models; research integrity; responsible artificial intelligence; scientific publishing ethics
    DOI:  https://doi.org/10.1016/j.bja.2026.01.029
  7. Proc Natl Acad Sci U S A. 2026 Mar 03. 123(9): e2526734123
    Academic journals' AI policies fail to curb the surge in AI-assisted academic writing.
    Yongyuan He, Yi Bu.
      The rapid integration of generative AI into academic writing has prompted widespread policy responses from journals and publishers. However, the effectiveness of these policies remains unclear. Here, we analyze 5,114 journals and over 5.2 million papers to evaluate the real-world impact of AI usage guidelines. We show that despite 70% of journals adopting AI policies (primarily requiring disclosure), researchers' use of AI writing tools has increased dramatically across disciplines, with no significant difference between journals with or without policies. Non-English-speaking countries, physical sciences, and high-OA journals exhibit the highest growth rates. Crucially, full-text analysis on 164 k scientific publications reveals a striking transparency gap: Of the 75 k papers published since 2023, only 76 (~0.1%) explicitly disclosed AI use. Our findings suggest that current policies have largely failed to promote transparency or restrain AI adoption. We urge a reevaluation of ethical frameworks to foster responsible AI integration in science.
    Keywords:  academic journal; artificialintelligence; publishing
    DOI:  https://doi.org/10.1073/pnas.2526734123
  8. AMIA Annu Symp Proc. 2024 ;2024 1549-1556
    Detecting Reference Errors in Scientific Literature with Large Language Models.
    Tianmai M Zhang, Neil F Abernethy.
      Reference errors, such as citation and quotation errors, are common in scientific papers. Such errors can result in the propagation of inaccurate information, but are difficult and time-consuming to detect, posing a significant threat to the integrity of scientific literature. To support automatic detection of reference errors, we evaluated the ability of large language models in OpenAI's GPT family to detect quotation errors. Specifically, we prepared an expert-annotated, general-domain dataset of statement-reference pairs from journal articles, one-third of which is in biomedicine. Large language models were evaluated in different settings with varying amounts of reference information provided by retrieval augmentation. Results showed that large language models are able to detect erroneous citations with limited context and without fine-tuning. This study contributes to the growing literature that seeks to utilize artificial intelligence to assist in the writing, reviewing, and publishing of scientific papers as well as grounding of language model responses.
  9. Postgrad Med J. 2026 Feb 25. pii: qgag020. [Epub ahead of print]
    Peer review and the limits of artificial intelligence (Letter in response to "Peer review in the age of artificial intelligence: a comparative study of human and AI-generated review reports").
    Shigeki Matsubara.
      
    Keywords:  ChatGPT; artificial intelligence; peer review; publication; review
    DOI:  https://doi.org/10.1093/postmj/qgag020
  10. Health Aff Sch. 2026 Feb;4(2): qxag028
    Using AI to improve peer review and research integrity in scientific journals.
    Howard Bauchner, Frederick Rivara.
      Peer review is inefficient, biased, and often ineffective. However, its importance in maintaining research integrity, at a time when the public has less faith in science, is clear, since journals are the principal conduit for communicating the results of research to the scientific community and the public. Given that the number of published manuscripts in the biomedical sciences now exceeds 3 million per year it is no longer possible for human editorial and peer review alone to ensure integrity. New approaches are needed, including the use of artificial intelligence (AI) to assist in editorial and peer review.
    Keywords:  AI; peer review; research integrity
    DOI:  https://doi.org/10.1093/haschl/qxag028
  11. Nature. 2026 Feb 23.
    This AI can improve your peer review - and make it more polite.
    Nicola Jones.
      
    Keywords:  Machine learning; Peer review
    DOI:  https://doi.org/10.1038/d41586-026-00536-6
  12. Postgrad Med J. 2026 Feb 26. pii: qgag018. [Epub ahead of print]
    Can ChatGPT effectively generate abstracts in orthopedic surgery? A comparative analysis between human-written and ChatGPT-generated scientific abstracts.
    Hong Jin Kim, Jinu Yoon, Tae Sik Goh, Jun-Ki Moon, Hyungtae Kim, Sun-Hyung Lee.
       BACKGROUND: Despite growing interest in using Chat-based Generative Pre-trained Transformer (ChatGPT) for academic writing, limited evidence exists regarding its ability to generate abstracts that are structurally compliant and ethically acceptable in orthopedic surgery.
    OBJECTIVE: To assess the performance of ChatGPT-generated abstracts using only article titles from recent publications in major orthopedic journals.
    METHODS: We extracted 90 human-written abstracts from three leading orthopedic journals and used each title to generate abstracts with ChatGPT-3.5 and ChatGPT-4.0. A total of 180 AI-generated abstracts were created using a standardized prompt. Each abstract was evaluated for format compliance, adherence to word limit, word count, consistency in study design, sample size correlation, and conclusion relevance. Plagiarism and AI detectability were assessed. Four orthopedic surgeons independently reviewed a subset of abstracts to identify their source.
    RESULTS: GPT-4.0 achieved perfect compliance with journal format and word count, while GPT-3.5 met these criteria in 34.4% (31 of 90) and 86.7% (78 of 90) of cases, respectively (P < .001). However, only half of abstracts presented fully relevant conclusions. Plagiarism was flagged in 45% to 70% of cases across both detection programs. AI detection scores were significantly higher in GPT-generated abstracts than for human-written ones (P < .001). Human reviewers showed limited ability to distinguish between human and AI-generated abstracts, with minimal inter-rater agreement (Cohen's kappa = 0.25).
    CONCLUSION: Although ChatGPT, particularly GPT 4.0, can generate abstracts that meet structural requirements and reproduce surface-level elements of academic style, significant limitations remain in content accuracy, originality, and ethical considerations. Key messages What is already known on this topic: With the expanding application of artificial intelligence (AI) techniques, the development of large language models (LLMs) has enabled the generation of natural language with enhanced performance, driven by improved context handling, broader multimodal capabilities, and optimized architectures. However, their specific capacity to generate structurally compliant and ethically acceptable abstracts in the field of orthopedic surgery remains unclear. What this study adds: This study demonstrates that while GPT-4.0 achieves superior adherence to formatting and word counts compared to GPT-3.5, both models frequently generate inaccurate conclusions and exhibit high plagiarism rates, despite being difficult for human reviewers to distinguish from human-written text. How this study might affect research, practice, or policy: Although ChatGPT shows potential as a supportive tool for generating orthopedic research abstracts, our overall findings emphasize that its unregulated or exclusive use introduces significant ethical and practical concerns. To ensure the integrity of academic publishing, it is imperative to establish clear, field-specific guidelines that govern the responsible application of LLMs in scientific writing.
    Keywords:  ChatGPT; academic writing; artificial intelligence; large language model; orthopedics
    DOI:  https://doi.org/10.1093/postmj/qgag018
  13. EMBO Rep. 2026 Feb 26.
    How do authors want to use AI for review? : A survey to assess the perception of scientists who received both AI and human reviews of their manuscripts.
    Thomas Lemberger, Niv Samuel Mastboim, Oded Rechavi.
      
    DOI:  https://doi.org/10.1038/s44319-026-00725-4
  14. AJR Am J Roentgenol. 2026 02 25.
    Increasing Use of Large Language Models for Scholarly Writing in Radiology.
    Vidith Phillips, Ammar A Pallikonda Latheef, Suryadipto Sarkar, Aditya Kulkarni, Pritam Mukherjee, Paul H Yi.
      
    DOI:  https://doi.org/10.2214/AJR.25.34273
  15. Nature. 2026 Feb;650(8103): 1066-1067
    Five ways to spot when a paper is a fraud.
    Stephanie Melchor.
      
    Keywords:  Peer review; Publishing; Scientific community; Technology
    DOI:  https://doi.org/10.1038/d41586-026-00569-x
  16. Harefuah. 2026 Feb;165(2): 91-94
    [The Interplay between Peer Review and the Quality and Reliability of Scientific Publications: Research Innovations].
    Zvi Weizman, Yehuda Shoenfeld.
       INTRODUCTION: Maintaining an appropriate scientific standard of manuscripts by editors of journals and books requires a respectable peer review system. Conversely, maintaining a rigorous peer-review process enables the publication of high-quality, reliable scientific work. All of these pose significant challenges for researchers, authors, and editors, particularly in light of the rapid technological developments and the integration of artificial intelligence tools into the scientific environment. Alongside these, there is a growing phenomenon of promoting the publication of fraudulent articles by illegal entities. These processes have encouraged extensive scientific research in these areas in recent years. One of the main platforms for presenting and discussing these topics is the "International Congress on Peer Review and Scientific Publication." In this publication, we will summarize the main research conducted in recent years in these areas, based primarily on the innovative material presented at the congress.
  17. PLoS Biol. 2026 Feb;24(2): e3003650
    Screening, sorting, and the feedback cycles that imperil peer review.
    Carl T Bergstrom, Kevin Gross.
      Scholarly journals rely on peer review to identify the science most worthy of publication. Yet finding willing and qualified reviewers to evaluate manuscripts has become an increasingly challenging task, possibly even threatening the long-term viability of peer review as an institution. What can or should be done to salvage it? Here, we develop mathematical models to reveal the intricate interactions among incentives faced by authors, reviewers, and readers in their endeavors to identify the best science. Two facets are particularly salient. First, peer review partially reveals authors' private sense of their work's quality through their decisions of where to send their manuscripts. Second, journals' reliance on traditionally unpaid and largely unrewarded review labor deprives them of a standard market mechanism-wages-to recruit additional reviewers when review labor is in short supply. We highlight a resulting feedback loop that threatens to overwhelm the peer review system: (1) an increase in submissions overtaxes the pool of suitable peer reviewers; (2) the accuracy of review drops because journals must either solicit assistance from less qualified reviewers or ask current reviewers to do more; (3) as review accuracy drops, submissions further increase as more authors try their luck at venues that might otherwise be a stretch. We illustrate how this cycle is propelled by the increasing emphasis on high-impact publications, the proliferation of journals, and competition among these journals for peer reviews. Finally, we suggest interventions that could slow or even reverse this cycle of peer-review meltdown.
    DOI:  https://doi.org/10.1371/journal.pbio.3003650
  18. MCN Am J Matern Child Nurs. 2026 Mar-Apr 01;51(2):51(2): 60
    Putting the "Peer" Back in Peer Review.
    Nicholas Raposo.
      
    DOI:  https://doi.org/10.1097/NMC.0000000000001174
  19. Emerg Med Australas. 2026 Feb;38(1): e70232
    The Current State of Peer Review.
    W Kenneth Milne, Daniel Fatovich.
      Peer review is a cornerstone of modern scientific publishing, yet its current form is a relatively recent development that became institutionalised in the mid-20th century. While intended to ensure rigour, credibility and quality control, the peer review system now faces mounting pressures. Rapid growth in manuscript submissions, reliance on unpaid reviewer labour, reviewer fatigue and the rise of predatory journals have strained its effectiveness. These challenges contribute to delays, inconsistent review quality and increasing retraction rates, highlighting vulnerabilities that peer review often fails to detect at scale. Evidence from randomised trials suggests that most proposed reforms offer limited benefit, although adding statistical reviewers and adopting elements of open peer review produce modest improvements. Sustainable reform will require evidence-guided changes, better recognition of reviewer contributions, and careful integration of technological tools to preserve the integrity of scientific publishing.
    Keywords:  peer review; research;  publishing
    DOI:  https://doi.org/10.1111/1742-6723.70232
  20. Emerg Med J. 2026 Feb 27. pii: emermed-2026-215894. [Epub ahead of print]
    Refocusing peer review on science rather than formatting requirements.
    Michael Gottlieb.
      
    Keywords:  research
    DOI:  https://doi.org/10.1136/emermed-2026-215894
  21. Nature. 2026 Feb;650(8103): 1070
    The Contributor Role Taxonomy tool must serve to record extent of authorship.
    Xiaohui Xia.
      
    Keywords:  Authorship; Publishing; Research management
    DOI:  https://doi.org/10.1038/d41586-026-00591-z
  22. Am Surg. 2026 Feb 22. 31348261429461
    Holding the Scalpel: Scientific Authorship and Responsibility in the Era of Generative Artificial Intelligence.
    Javier Arredondo Montero.
      Scientific writing and authorship are fundamentally acts of professional judgment and responsibility. This essay examines these principles in the era of increasingly fluent generative artificial intelligence (AI), arguing that scientific integrity-much like surgical mastery-depends on a level of earned comprehension and accountability that no algorithm can simulate. Drawing on a surgical experience in a resource-limited setting to illustrate the nature of judgment under uncertainty, the piece explores how the rise of AI risks replacing genuine expertise with hollow fluency. The essay concludes that judgment and responsibility must remain irreducibly human in both surgical practice and scientific authorship.
    Keywords:  artificial intelligence; clinical judgment; medical ethics; scientific authorship; surgical decision-making
    DOI:  https://doi.org/10.1177/00031348261429461
  23. O G Open. 2026 Feb;3(1): e137
    Approaching the Blank Document: Gamification of Manuscript Writing From a Trainee's Perspective.
    Minhazur Sarker.
      Eventual conversion of completed research to a written manuscript can be daunting for a trainee; gamification of the process makes it digestible and approachable.
    DOI:  https://doi.org/10.1097/og9.0000000000000137
  24. Pol Arch Intern Med. 2026 Feb 27. pii: 17243. [Epub ahead of print]
    Artificial intelligence-assisted statistical analysis and statistical review: evidence (2023-2025) and implications for internal medicine.
    Michal Ordak.
      Clinical research published in internal medicine journals relies heavily on statistical analysis and quantitative inference, making the quality of statistical reporting and statistical peer review central to the credibility of this literature. Despite long-standing methodological recommendations, the quality of statistical analyses and reporting in medical journals remains suboptimal, and the proportion of manuscripts undergoing formal statistical review has not improved over recent decades. At the same time, generative artificial intelligence (AI) tools have been increasingly adopted in biomedical research, raising expectations that they may support statistical analysis and elements of the peer-review process. This narrative review synthesizes evidence published between 2023 and 2025 on the use of AI-assisted tools in statistical analysis and statistical review within medical research. The reviewed studies show that large language models can support selected tasks, including generation of analytical code, reproduction of simple statistical procedures, preliminary selection of statistical tests, and detection of certain formal statistical errors. However, AI performance is highly variable and frequently limited by incomplete consideration of statistical assumptions and reduced reliability in complex analytical scenarios. Current generative AI tools should not be regarded as fully autonomous instruments for statistical analysis or statistical peer review. Their effective use depends on statistical expertise, independent validation, and contextual judgment by human users. The review discusses implications for statistical practice and statistical review in internal medicine, a research setting characterized by heterogeneous observational data, multimorbidity, and frequent use of non-randomized study designs, including pragmatic clinical trials.
    DOI:  https://doi.org/10.20452/pamw.17243
  25. J Obstet Gynaecol Res. 2026 Mar;52(3): e70227
    Number of Authors in Japanese Obstetrics and Gynecology Case Reports: A Preliminary Observation.
    Shigeki Matsubara.
      
    Keywords:  author; author number; case reports; contribution; journal
    DOI:  https://doi.org/10.1111/jog.70227
  26. J Eval Clin Pract. 2026 Feb;32(1): e70391
    Older Doctors and Continuing to Write: An Evaluation Perspective.
    Shigeki Matsubara.
      
    DOI:  https://doi.org/10.1111/jep.70391
  27. JAMA Pediatr. 2026 Feb 24.
    Editor's Note on CONSORT-C and SPIRIT-C Guidelines.
    Dimitri A Christakis.
      
    DOI:  https://doi.org/10.1001/jamapediatrics.2026.0150
  28. Br J Gen Pract. 2026 Mar 01. 76(764): 106
    Authorship inflation in general practice research: unanswered questions to address?
    David Kernick.
      
    DOI:  https://doi.org/10.3399/bjgp26X744297
  29. Precis Chem. 2026 Feb 23. 4(2): 73-74
    Standardization Catalyzes Scientific Innovation.
    Juanjuan Jia, Jun Jiang.
      
    DOI:  https://doi.org/10.1021/prechem.5c00184
  30. Nature. 2026 Feb;650(8103): 1070
    Account for AI in the environmental footprint of scientific publishing.
    Giovanni Bacaro.
      
    Keywords:  Publishing; Scientific community; Sustainability
    DOI:  https://doi.org/10.1038/d41586-026-00590-0
  31. Clin Psychol Eur. 2025 Nov;7(4): e20861
    Behind the Doors: 24 Insights Into Scientific Publishing.
    Cornelia Weise.
      
    DOI:  https://doi.org/10.32872/cpe.20861
  32. Sci Data. 2026 Feb 24. pii: 301. [Epub ahead of print]13(1):
    PreprintToPaper dataset: connecting bioRxiv preprints with journal publications.
    Fidan Badalova, Julian Sienkiewicz, Philipp Mayr.
      The PreprintToPaper dataset connects bioRxiv preprints with their corresponding journal publications, enabling large-scale analysis of the preprint-to-publication process. It comprises metadata for 145,517 preprints from two periods, 2016-2018 (pre-pandemic) and 2020-2022 (pandemic), retrieved via the bioRxiv and Crossref APIs. We selected the two periods to capture preprint-publication dynamics before and during the COVID-19 pandemic while avoiding transitional years. Each record includes bibliographic information such as titles, abstracts, authors, institutions, submission dates, licenses, and subject categories, alongside enriched publication metadata including journal names, publication dates, author lists, and further information. In addition to the main dataset, a version-history subset provides all available versions of preprints within the two selected periods, enabling analysis of how preprints evolve over time. Preprints are categorized into three groups: Published (formally linked to a journal article), Preprint Only (posted on a preprint server), and Gray Zone (potentially published in a journal but unlinked). To enhance reliability, title and author similarity scores were computed, and a human-annotated subset of 299 records was created to evaluate Gray Zone cases. The dataset supports diverse applications, including studies of scholarly communication, open science policies, bibliometric tool development, and natural language processing research on textual changes between preprints and their  corresponding journal articles.
    DOI:  https://doi.org/10.1038/s41597-026-06867-3
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