bims-skolko Biomed News
on Scholarly communication
Issue of 2026–08–16
fifty-four papers selected by
Thomas Krichel, Open Library Society
  1. Against the current: Sole authorship as method in contemplative and conceptual scholarship.
  2. Seed Sown Outside Its Proper Soil: Rethinking Nature and Nurture from Dante to Developmental Science.
  3. The seductive predator: navigating predatory publishing and conferences.
  4. Thinking Outside the Black Box: Seven Guidelines for GenAI Use in the Ecological Sciences.
  5. Against AI-use declarations.
  6. Artificial Intelligence in Medical Writing: Subtle Errors and Their Complex Consequences.
  7. Updated Guidance for Author Use of AI in Medical Publication.
  8. Manuscript Review Best Practices.
  9. This AI tool claims to pick the top 1% of preprints. Should researchers trust it?
  10. When AI use becomes the norm: Researcher perspectives on AI disclosure policy and practice.
  11. Can Anthropic's invisible watermarks curb 'AI slop'? Researchers remain sceptical.
  12. Scientific Writing in the Age of Artificial Intelligence.
  13. Artificial Intelligence in Addiction Scholarship: Transparency, Accountability, and Disclosure in Research and Publishing.
  14. AI Writing Vocabulary in Ophthalmology: Faster Non-Native Adoption, Unchanged Outcomes.
  15. Beyond text generation: a comprehensive evaluation of ChatGPT-5.5 for scientific narrative review writing on sleeve gastrectomy.
  16. Enforcing generative AI use declarations: responding to false or incomplete disclosures.
  17. Artificial Intelligence Use in Medical Writing: On the Meaning and Limits of Disclosure.
  18. Generative Artificial Intelligence for Peer Review and Editorial Functions.
  19. Case Reports and Artificial Intelligence: Picking a Pearl among Abundant Reports.
  20. Artificial Intelligence and Academic Publishing.
  21. Artificial Intelligence in Scientific Writing - Our Editorial Position.
  22. Prompt injection compromises large language model-based peer review: evidence from randomised controlled trial abstracts from anaesthesia journals.
  23. Exploring views on remuneration for review: a survey of patient and public reviewers for The BMJ.
  24. Zero Risk Is Impossible: Confidentiality and Artificial Intelligence Use in Peer Review.
  25. Indigenous excellence: confronting racism and complicity in peer review.
  26. From Peer Review To Prompt Review.
  27. Rating Peer Reviews.
  28. Data sharing policies, requirements, and support from public and private clinical trial sponsors: a survey on top sponsors of clinical trials in Europe.
  29. Reporting Quality of Large Language Model Studies: A Cross-Sectional Audit of High-Ranking Radiology and Medical Imaging Journals.
  30. Concern About the Trends of Letters to Editors.
  31. Closing the gap between systematic review protocols and publication in anesthesiology: shared responsibility for transparency and accountability.
  32. Beyond the p -value: Pitfalls in Reporting and Applying Statistical Tests in Sports Science.
  33. Australian and New Zealand introduces the CONSIDER Statement as part of its peer review process.
  34. The desk review: practical considerations for authors from the editors' perspective.
  35. Deviations between registered protocols and published systematic reviews in high-impact anesthesiology and pain medicine journals: a cross-sectional analysis.
  36. Writing the hard way.
  37. The Open Data Commons for Traumatic Brain Injury: An Interagency Community Platform for Pre-Clinical Data Management and Sharing.
  38. Inadequate Reporting of Harms in RCTs of Chinese Herbal Medicine Formulas: A Systematic Survey of Leading Medical Journals.
  39. Writing a Case Report or Case Series: Practical Guidance for Clinicians and Researchers.
  40. Writing Difference: Deficit Discourse and Epistemic Responsibility in Nursing Scholarship.
  41. Evaluating Evidence-Based Practice and Quality Improvement Initiatives in the Nursing Literature Using a Critical Appraisal Tool.
  42. Pitfalls of announcing regulatory policy in medical journals.
  43. Strengthening the JMI-SPIE Medical Imaging Connection.
  44. 75 Years of Adventure in Turkish Scientific Medical Publishing: From Istanbul Contribution to Clinical Science to Turkish Journal of Hematology (1951-2026).
  45. Introduction of a Journal of Shoulder and Elbow Surgery manuscript processing fee.
  46. Share Your Research in Experimental Hematology-Announcing Exciting New Article Types!
  47. Making an Impact with the Indian Journal of Critical Care Medicine.
  48. A decade of impact: the Journal of Spine Surgery enters its next phase.
  49. Generative artificial intelligence in manuscript writing and peer review for the Journal of Neuroimmunology.
  50. A journal that grows older and stays young.
  51. Reflections on Two Years as Mentored Editor of the Journal of Nutrition Education and Behavior.
  52. Where Does JKNS Stand in JCR 2025, and How Should We Proceed for Sustainable Growth?
  53. Editorial: 30 Years of Health.
  54. Journey of the Journal of Mid-Life Health - Continuing the Legacy.
  1. Account Res. 2026 Aug 10. 2714974
    Against the current: Sole authorship as method in contemplative and conceptual scholarship.
    Heather Kempton.
      In the context of escalating authorship inflation and growing concern about research integrity, this article examines sole authorship as an epistemically and ethically grounded methodological condition within specific forms of contemplative and conceptual scholarship. Focusing on forms of inquiry in which interpretation constitutes the primary analytic process, I argue that sole authorship supports a continuous and traceable interpretive arc, central to epistemic integrity. In such contexts, continuity of interpretation not only enables conceptual coherence but also underpins ethical accountability by ensuring that responsibility for interpretive decisions remains clearly identifiable across the analytic process. Framed as a contemplative method grounded in mindful solitude, understood as an intentional orientation toward inward integration, sole authorship is situated within contemplative psychology and extended to methodologies such as Intuitive Inquiry, heuristic inquiry, hermeneutic phenomenology, and autoethnography, where interpretive validity depends on a sustained authorial perspective. While collaborative research remains essential across many domains, this article identifies conditions under which a unified authorial voice provides methodological advantages not fully reproduced through distributed authorship. The argument is therefore not that sole authorship offers a general solution, but that it functions as a context-specific stance aligned with the epistemic and ethical demands of contemplative scholarship.
    Keywords:  Mindful solitude; authorship ethics; contemplative scholarship; epistemic integrity; research integrity; sole authorship
    DOI:  https://doi.org/10.1080/08989621.2026.2714974
  2. J Genet Psychol. 2026 Aug 14. 1-5
    Seed Sown Outside Its Proper Soil: Rethinking Nature and Nurture from Dante to Developmental Science.
    Gianluca Esposito, Meng Xianwei, Silvia Perzolli.
      Scientific articles typically present research as a coherent sequence of hypotheses, methods, and findings, leaving little room for the intellectual journey that precedes publication. This article inaugurates Behind the Scenes, a new feature of The Journal of Genetic Psychology that explores the ideas, challenges, unexpected discoveries, and critical decisions that shape scientific research beyond the published manuscript. Inspired by Dante Alighieri's reflection on the interplay between innate predispositions and environmental influences in Paradiso VIII, we introduce an interview with Dr Xianwei Meng, first author of How Does the Mind Grow? Cross-Cultural Intuitive Theories of Mental Development. The conversation traces the evolution of the project from its original conception to its theoretical and methodological turning points, highlighting the role of cross-cultural collaboration, open science, and intellectual flexibility in advancing developmental research. More broadly, the interview illustrates how unexpected findings can redirect scientific inquiry and generate new theoretical perspectives. By offering readers a glimpse into the creative and often nonlinear process behind influential research, Behind the Scenes seeks to foster a deeper appreciation of scientific discovery while inspiring the next generation of developmental scientists.
    Keywords:  Nature and nurture; developmental science; developmental systems; intuitive theories; mental development; social cognition
    DOI:  https://doi.org/10.1080/00221325.2026.2717336
  3. Occup Med (Lond). 2026 Aug 10. pii: kqag075. [Epub ahead of print]
    The seductive predator: navigating predatory publishing and conferences.
    Nerys Williams.
      
    DOI:  https://doi.org/10.1093/occmed/kqag075
  4. Ecol Evol. 2026 Aug;16(8): e74118
    Thinking Outside the Black Box: Seven Guidelines for GenAI Use in the Ecological Sciences.
    Nina C Ferrari, Emily E Conklin, Amelia A Fitch, Dustin G Gannon, Rossana Macedo, Aidan Place, Hannah R Sachs, Eugene Seo, Matthew J Weldy, Matthew G Betts.
      Generative artificial intelligence (GenAI) tools are increasingly incorporated into ecological workflows at every stage of the scientific process, from hypothesis generation and data collection to synthesis, analysis, manuscript preparation, and peer review. This rapid integration presents new opportunities for discovery but also poses fundamental challenges to the epistemological foundations of the field. We discuss how the uncritical adoption of GenAI risks eroding core principles of ecological inquiry, including idea generation, reproducibility, transparency, and engagement with natural systems. By prioritizing scale, speed, and statistical pattern detection over rigorous theory development and testing, GenAI may shift research away from theory-driven questions and toward large-scale data-mining approaches. GenAI tools offer clear benefits when used thoughtfully; these include expanded access to information and the ability to synthesize across otherwise disparate disciplines and datasets. But overemphasis on these approaches could have adverse impacts on the field, including reducing reproducibility and ecological inference and reinforcing existing inequities in knowledge production. Here, we identify key challenges associated with GenAI integration in the field of ecology and provide seven actionable guidelines to support its responsible and effective use. We call for a concerted effort to engage in reflective and intentional use of GenAI in ecological research, resisting pressures to prioritize efficiency over the rigor, creativity, inclusivity, and societal relevance of our field.
    Keywords:  ecology; generative artificial intelligence; large language models; reproducibility; research ethics; scientific workflows
    DOI:  https://doi.org/10.1002/ece3.74118
  5. J Med Ethics. 2026 Aug 14. pii: jme-2026-112386. [Epub ahead of print]
    Against AI-use declarations.
    Zeljka Buturovic, Vojin Rakic.
      
    Keywords:  Ethics; Ethics, Research; Ethics- Research; Philosophy; Scientific Misconduct
    DOI:  https://doi.org/10.1136/jme-2026-112386
  6. JMA J. 2026 Jul 15. 9(4): 992-996
    Artificial Intelligence in Medical Writing: Subtle Errors and Their Complex Consequences.
    Shigeki Matsubara, Daisuke Matsubara, Kazuhiko Kotani.
      Although artificial intelligence (AI; such as ChatGPT) can improve manuscript readability, AI-related false descriptions (so-called hallucinations) and incorrect reference retrieval have been repeatedly reported. We tested (1) whether ChatGPT, when provided with medically correct inputs, generates a manuscript containing medically incorrect statements-particularly whether it misunderstands or overlooks "subtle but important" medical issues-and (2) whether ChatGPT cites inappropriate references. We input bullet points on placenta percreta, tasked ChatGPT-5 with generating a mini-review, and asked it to confirm whether the output was medically correct. We introduced a small, deliberate trap. In percreta, a recent conceptual change has gained increased attention: this condition is considered to result from uterine abnormality rather than abnormal placental invasion. This etiopathological shift could be misinterpreted as implying "weaker adherence" and therefore "less difficult surgery," leading to the notion that percreta could be managed at secondary-level institutions. The ChatGPT-generated manuscript cited appropriate references and was almost medically correct, except for one critical issue: it stated that percreta could be managed in secondary-level institutions, which is incorrect and potentially dangerous. During the "confirmation" stage, ChatGPT raised a caution regarding this issue, but not in a definitive manner. An additional experiment was conducted on hypercholesterolemia. ChatGPT again failed to address an important issue, familial hypercholesterolemia, which requires a management strategy different from that for non-familial hypercholesterolemia. Overall, ChatGPT generated a linguistically appealing manuscript with largely correct context, but it produced incorrect and potentially dangerous statements in clinically critical areas. When incorrect statements are subtle rather than obvious, authors, journals, and readers may fail to recognize them. This is paradoxical: advances in AI may reduce "apparent" errors while generating less recognizable ones. When evaluating AI-assisted manuscripts, careful review by individuals with deep domain knowledge is mandatory.
    Keywords:  ChatGPT; artificial intelligence; bullet points; reference; review
    DOI:  https://doi.org/10.31662/jmaj.2026-0030
  7. JAMA. 2026 08 10.
    Updated Guidance for Author Use of AI in Medical Publication.
    Annette Flanagin, Roy H Perlis, Kirsten Bibbins-Domingo.
      
    DOI:  https://doi.org/10.1001/jama.2026.16613
  8. West J Nurs Res. 2026 Sep;48(9): 907-908
    Manuscript Review Best Practices.
    Todd M Ruppar.
      
    DOI:  https://doi.org/10.1177/01939459261476797
  9. Nature. 2026 Aug 10.
    This AI tool claims to pick the top 1% of preprints. Should researchers trust it?
    Miryam Naddaf.
      
    Keywords:  Machine learning; Publishing
    DOI:  https://doi.org/10.1038/d41586-026-02276-z
  10. Account Res. 2026 Aug 13. 2716884
    When AI use becomes the norm: Researcher perspectives on AI disclosure policy and practice.
    Ayoung Yoon, Siena Oristaglio.
       BACKGROUND: Despite the proliferation of AI disclosure requirements in academic publishing, recent research suggests a persistent gap between policy expectations and research practice. However, little is known about how researchers perceive and navigate these requirements or what limitations they identify in current disclosure practices.
    METHOD: This study explored researchers' experiences with AI disclosure through semi-structured interviews with 14 researchers from two interdisciplinary fields, bioinformatics and computational social science. Data were analyzed using reflexive thematic analysis.
    RESULTS: Four thematic groupings emerged: fragmented and inconsistently enforced requirements; systemic limitations, including scope ambiguity, research integrity risks, and structural disincentives to honest reporting; researcher perspectives on more effective disclosure practices; and disciplinary variation as a cross-cutting dimension shaping how these issues are experienced across research communities. The findings suggest that the compliance gap reflects an interaction between structural conditions and ethical obligations. This gap is sustained by self-reporting mechanisms that lack verification capacity, a transparency paradox in which honest disclosure can invite professional penalization, and disciplinary norms that resist uniform governance approaches.
    CONCLUSIONS: The study provides empirical evidence supporting the development of a structured AI contribution taxonomy as a more principled and practical alternative to existing disclosure practices. More broadly, the findings suggest that effective AI disclosure governance should incorporate field-sensitive adaptation rather than relying on uniform implementation across diverse research communities.
    Keywords:  AI disclosure; Artificial intelligence; research integrity; scholarly publishing
    DOI:  https://doi.org/10.1080/08989621.2026.2716884
  11. Nature. 2026 Aug 13.
    Can Anthropic's invisible watermarks curb 'AI slop'? Researchers remain sceptical.
    Elizabeth Gibney.
      
    Keywords:  Computer science; Machine learning; Publishing
    DOI:  https://doi.org/10.1038/d41586-026-02503-7
  12. JMA J. 2026 Jul 15. 9(4): 1019-1020
    Scientific Writing in the Age of Artificial Intelligence.
    Soichiro Saeki.
      
    Keywords:  academic publishing; artificial intelligence; linguistic diversity; postgraduate education and training; scholarly communication
    DOI:  https://doi.org/10.31662/jmaj.2026-0083
  13. Subst Use Addctn J. 2026 Aug 13. 29767342261471638
    Artificial Intelligence in Addiction Scholarship: Transparency, Accountability, and Disclosure in Research and Publishing.
    Adam J Gordon, Mark Bounthavong, Colleen Corte, Elizabeth Siantz, Khadejah F Mahmoud, Deborah S Finnell, Taneisha Scheuermann, Evans F Kyei, Jessica J Wyse, Elizabeth M Oliva, Marianne Pugatch, Rebekah S Halmo, Sarah Rosenwohl-Mack, Sarah J Marks, Babalola Faseru.
      Artificial intelligence (AI) is embedded in addiction scholarship, not only as a methodological tool in research but also as a tool for manuscript preparation. In addiction research and clinical care, AI is already used to analyze data and support clinical decision-making. In parallel, AI tools are increasingly used to summarize literature, revise prose, generate outlines, draft text, and assist with interpretation and presentation of findings. These developments create an urgent need for addiction journals to clarify how AI use should be governed and disclosed. In this editorial, we argue for a principled, proportionate framework for AI disclosure grounded in materiality. We distinguish AI used in the conduct of research from AI used in manuscript preparation and propose a practical taxonomy of assistive, intermediate, and generative AI mapped onto 6 escalating levels of involvement. We identify intermediate AI as a key governance challenge because it may appear to provide editorial assistance while materially shaping scholarly content. We argue that disclosure should be required when AI materially contributes to the conduct of research or to the intellectual content, interpretation, argument, or presentation of a manuscript, while routine low-risk assistive uses should not require disclosure. Transparent governance, organized around materiality rather than specific technologies, is the appropriate response to AI in addiction publishing.
    Keywords:  addiction medicine; artificial intelligence; editorial; editorial policies; generative artificial intelligence; large language models; medical writing; substance-related disorders; writing
    DOI:  https://doi.org/10.1177/29767342261471638
  14. Am J Ophthalmol. 2026 Aug 12. pii: S0002-9394(26)00453-8. [Epub ahead of print]
    AI Writing Vocabulary in Ophthalmology: Faster Non-Native Adoption, Unchanged Outcomes.
    Alessandro Berni, Daniel Shu Wei Ting, Greta Caputo, Alessandro Russo, Laura Gutierrez Sinisterra, Alessandro Avitabile, Gianni Virgili, Michele Reibaldi, Fabrizio Giansanti, Daniela Bacherini, Enrico Borrelli.
       PURPOSE: To quantify the change in large language model (LLM)-associated writing vocabulary in ophthalmology after ChatGPT, to test whether it differed by first-author affiliation-country language group, and to determine whether publishing outcomes shifted.
    DESIGN: Retrospective, cross-sectional bibliometric study, with an interrupted time-series analysis of a 10-year publication census.
    SUBJECTS, PARTICIPANTS, AND/OR CONTROLS: Published articles, not human subjects. Primary corpus, 15,683 PubMed abstracts from the 40 highest-impact ophthalmology journals (top 10 per 2025 Journal Citation Reports quartile), pre-ChatGPT (2018-2019) versus post-ChatGPT (2023-2024); supportive corpus, 6,139 open-access full texts; and a 48,468-article census (2015-2024) for outcomes. Articles were grouped by whether the first author's affiliation country was native-English-speaking.
    METHODS, INTERVENTION, OR TESTING: We counted 41 curated LLM-associated excess words per document, with the word count as a Poisson offset and frequency-common control words for specificity. The prespecified primary analysis was the period-by-language-group interaction in a Poisson generalized estimating equation clustered on first author. The instrument was construct-validated against 30 LLM-generated abstracts and against pre-ChatGPT, definitionally LLM-free, human abstracts.
    MAIN OUTCOME MEASURES: The period-by-language-group interaction incidence rate ratio (IRR) for the excess-word rate; and the non-native share of publications and of top-quartile journal placements.
    RESULTS: The excess-word rate rose 2.1-fold, from 320 to 668 per million words. The rise was steeper for non-native-English-affiliated first authors (interaction IRR, 0.61; 95% CI, 0.48-0.78; P < .001), robust to adjustment for journal quartile and country income, to excluding China (IRR, 0.65), and in an independent full-text corpus (IRR, 0.64). The counter separated LLM-generated from human abstracts (area under the receiver operating characteristic curve [AUC], 0.88), whereas per-article discrimination was near chance (AUC, 0.53), confirming a population-level rate rather than a classifier. An interrupted time series showed no post-ChatGPT change in the non-native share of publications or of top-quartile journals.
    CONCLUSIONS: After ChatGPT, non-native-English-affiliated authors in ophthalmology adopted AI-associated writing vocabulary faster than native-affiliated authors, without any accompanying gain in publication frequency or journal placement. The measure reflects population-level AI-associated style, not fluency, quality, or confirmed AI use, and should not be used to classify individual articles.
    Keywords:  bibliometrics; large language models; non-native English speakers; publication equity; research integrity; scientific writing
    DOI:  https://doi.org/10.1016/j.ajo.2026.08.011
  15. Updates Surg. 2026 Aug 12.
    Beyond text generation: a comprehensive evaluation of ChatGPT-5.5 for scientific narrative review writing on sleeve gastrectomy.
    Akin Calisir, Mustafa Sahin, Fatih Turkoglu, Sinan Sener, Abdullah Gurhan Duyan, Husnu Alptekin.
      Large language models are increasingly being used in academic and scientific writing, but their reliability in medical literature generation remains uncertain. In particular, concerns persist regarding factual accuracy, reference validity, originality, and the overall academic quality of AI-assisted manuscripts. This study aimed to systematically evaluate the capacity of ChatGPT-5.5 to generate a scientific narrative review on sleeve gastrectomy using structured prompting and objective assessment criteria. ChatGPT-5.5 was instructed to generate a narrative review on long-term metabolic improvement and weight loss outcomes after sleeve gastrectomy using a structured prompt framework. The generated manuscript was independently evaluated by two general surgeons with expertise in bariatric and metabolic surgery. Assessment domains included scientific accuracy, reference validity, plagiarism screening, narrative review quality using the Scale for the Assessment of Narrative Review Articles (SANRA), and academic quality using a structured peer-review rubric. The model generated a structured nine-section review outline and a complete narrative review manuscript. Overall, 85 statements were identified and evaluated. Of the 40 cited statements, 38 were factually correct and 2 were factually incorrect. The remaining 45 uncited statements were also considered factually correct, although 16 were classified as requiring supporting references. Reference verification revealed that 16 of 33 references (48.48%) were fully verifiable, whereas 17 references (51.52%) were inaccurate or unverifiable, including 3 entirely fabricated citations. iThenticate analysis showed an overall similarity score of 14%, with no substantial evidence of plagiarism on manual review. SANRA scores were 7 and 8 out of 12, while structured peer-review rubric scores were 37 and 36 out of 50, indicating overall good manuscript quality. Inter-rater agreement was excellent for both SANRA and rubric scoring. In this structured evaluation, ChatGPT-5.5 generated a narrative review on sleeve gastrectomy that was generally well structured, factually accurate, original, and of acceptable academic quality. These findings suggest that large language models may have a supportive role in surgical academic writing, particularly in organizing review content and generating coherent scientific text. However, important limitations were identified in reference validity, including bibliographic inaccuracies and fabricated citations. These findings suggest that large language models may support scientific writing when guided by structured prompts, but their outputs require expert oversight, reference verification, and critical validation before use in academic publishing.
    Keywords:  Artificial intelligence; ChatGPT; Narrative review; Reference validity; Sleeve gastrectomy
    DOI:  https://doi.org/10.1007/s13304-026-02802-8
  16. J Med Ethics. 2026 Aug 12. pii: jme-2026-112358. [Epub ahead of print]
    Enforcing generative AI use declarations: responding to false or incomplete disclosures.
    Daniel Rodger, Christopher A Bobier.
      
    Keywords:  Ethics, Research; Scientific Misconduct
    DOI:  https://doi.org/10.1136/jme-2026-112358
  17. JMA J. 2026 Jul 15. 9(4): 997-1000
    Artificial Intelligence Use in Medical Writing: On the Meaning and Limits of Disclosure.
    Shigeki Matsubara.
      This opinion paper focuses on disclosure of artificial intelligence (AI) use in medical writing; AI use beyond manuscript writing is outside its scope. Almost all journals require full disclosure of AI use. However, several barriers may hinder such disclosure. First, there is a lack of clear statements by journals on how disclosure is evaluated. Authors may be concerned that a linguistically polished manuscript accompanied by AI disclosure might be scored lower. Conversely, a journal might question the nonuse of AI assistance when a manuscript presents highly significant data written in uncomfortable English. Authors may fear that either AI "use" or "nonuse" could result in disadvantage. Second, criteria for disclosure remain unclear. Many journals require disclosure when AI is used to "create, review, revise, or edit" but not when it is used for "checking grammar, spelling, and similar tasks." However, the distinction between "edit" and "grammar check" is a delicate one; prompts such as "edit" and "check grammar" may produce almost identical revisions. Third, it may become difficult to distinguish between one's original expressions and AI-aided revisions after several rounds of linguistic consultation. Authors may regard the final text as their own authorship rather than AI intervention. These three factors, intentionally or unintentionally, may prevent honest and full disclosure. Journals may consider stating two points. First, that the use or nonuse of AI tools does not affect acceptance or rejection, provided that such use adheres to journal guidelines and is accurately disclosed. Second, that disclosure information is accumulated as a resource for future analysis, contributing to a more transparent publication environment. Such clarification could reduce hesitation surrounding disclosure. Transparency, from both authors and journals, would become an asset to the medical community.
    Keywords:  artificial intelligence; disclosure; guideline; journal; regulation
    DOI:  https://doi.org/10.31662/jmaj.2026-0121
  18. JMA J. 2026 Jul 15. 9(4): 1021-1022
    Generative Artificial Intelligence for Peer Review and Editorial Functions.
    Jaime A Teixeira da Silva.
      
    Keywords:  editorial functions; generative artificial intelligence; peer review; science publishing
    DOI:  https://doi.org/10.31662/jmaj.2026-0170
  19. JMA J. 2026 Jul 15. 9(4): 1025-1026
    Case Reports and Artificial Intelligence: Picking a Pearl among Abundant Reports.
    Shigeki Matsubara.
      
    Keywords:  ChatGPT; artificial intelligence; case report
    DOI:  https://doi.org/10.31662/jmaj.2026-0135
  20. Emerg Med Australas. 2026 Aug;38(4): e70322
    Artificial Intelligence and Academic Publishing.
    Geoff Hughes, Daniel Fatovich.
      
    DOI:  https://doi.org/10.1111/1742-6723.70322
  21. J Thorac Oncol. 2026 Aug;pii: S1556-0864(26)00403-X. [Epub ahead of print]21(8): 103950
    Artificial Intelligence in Scientific Writing - Our Editorial Position.
    Emily Stone, Alex A Adjei.
      
    DOI:  https://doi.org/10.1016/j.jtho.2026.103950
  22. Br J Anaesth. 2026 Aug 11. pii: S0007-0912(26)00529-5. [Epub ahead of print]
    Prompt injection compromises large language model-based peer review: evidence from randomised controlled trial abstracts from anaesthesia journals.
    Alessandro De Cassai, Burhan Dost, Elisa Pistollato, Francesco Zarantonello, Annalisa Boscolo, Paolo Navalesi.
      
    Keywords:  artificial intelligence; ethics; large language model; peer review; prompt injection; research integrity
    DOI:  https://doi.org/10.1016/j.bja.2026.06.041
  23. BMJ Open. 2026 Aug 13. 16(8): e110983
    Exploring views on remuneration for review: a survey of patient and public reviewers for The BMJ.
    Sara Schroter, Rebecca Harmston, Emma Doble, Sophie Cook, Amy Price.
       OBJECTIVES: Calls to compensate patient partners for contributions to the health sector are increasing. The BMJ invites patients and the public (P&P) to review manuscripts alongside academic reviewers and recently introduced remuneration. We surveyed P&P reviewers to capture perspectives on remuneration and overall reviewing experience.
    DESIGN/SETTING: Two cross-sectional surveys administered via SurveyMonkey to P&P reviewers for The BMJ journal.
    PARTICIPANTS: To capture views from those familiar with reviewing for The BMJ and views from those less familiar, we conducted two surveys. Survey 1 was sent to 267 reviewers who had completed a review in the past 3 years. Survey 2 was sent to 493 reviewers who had been invited to review but not completed a review within the past 3 years.
    RESULTS: Survey 1 received 183/267 (69%) responses; survey 2 received 100/493 (20%) responses. Most respondents were based in the UK or the USA. Overall, 71% (202/283) rated their review experience as 'very good' or 'good'. Half (51%, 143/283) said a £50 payment would make them more likely to review (48% survey 1, 56% survey 2). One-third (32%, 91/283) said a subscription to a selection of BMJ journals would make them more likely to review (32% survey 1, 33% survey 2). However, 29% (82/283) said £50 would not influence them (33% survey 1, 22% survey 2) and 40% (114/283) said the same about a subscription (39% survey 1, 43% survey 2). Views on remuneration varied-some saw it as recognition of value, others viewed it as unnecessary and some felt it was inadequate compensation. While 59% (166/283) had no concerns about introducing payment, 18% (52) had concerns, and 17% (49) were unsure. Concerns included potentially changing reviewers' motivations and the quality of reviews, administrative burden and tax implications, impact on income received from benefits and a need to evaluate the initiative. Respondents emphasised the importance of offering optional incentives to accommodate individual preferences.
    CONCLUSIONS: The BMJ's P&P reviewers hold diverse views on remuneration. Flexible, optional incentives may help support broader engagement while respecting individual needs and values.
    Keywords:  Patient Participation; Patients; Surveys and Questionnaires
    DOI:  https://doi.org/10.1136/bmjopen-2025-110983
  24. JMA J. 2026 Jul 15. 9(4): 989-991
    Zero Risk Is Impossible: Confidentiality and Artificial Intelligence Use in Peer Review.
    Shigeki Matsubara.
      Most journals' and publishers' guidelines regarding the use of artificial intelligence (AI) in peer review contain a similar statement: "reviewers are prohibited from uploading the manuscript to software or AI-assisted tools or technologies where confidentiality is not assured." I believe that reviewers should refrain from uploading not only the submitted manuscript but also their own review sheets to AI tools. This position is based on two reasons. First, even if AI tools state that uploaded data will be deleted upon request, actual deletion cannot be independently confirmed. Second, reviewers sometimes include highly confidential keywords or messages in the review sheet; uploading them may jeopardize confidentiality. My humble experiment indicates that even when deletion of uploaded data is requested, AI systems such as ChatGPT may retain materials for training purposes; thus, complete deletion is theoretically impossible. Authors are usually permitted to upload their own manuscripts for linguistic refinement. Although this may also jeopardize confidentiality, authors do so voluntarily and accept that risk. Reviewers, however, are entrusted with unpublished work and should therefore exercise far greater caution when considering the use of AI software. Editors and journals expect reviewers to evaluate the significance of the study and manuscript. The review sheet can, and perhaps should, be simple and straightforward, written in their own English. Because confidentiality cannot be independently verified and complete deletion cannot be guaranteed, reviewers should refrain from uploading manuscripts or review sheets to AI tools.
    Keywords:  ChatGPT; artificial intelligence; confidentiality; peer review; review
    DOI:  https://doi.org/10.31662/jmaj.2026-0060
  25. Health Promot Int. 2026 Jul 01. pii: daag117. [Epub ahead of print]41(4):
    Indigenous excellence: confronting racism and complicity in peer review.
    Raglan Maddox, Sarah Funnell, Michelle Kennedy, Janet Smylie.
      Racism in peer review is a common, structurally embedded, and systemic problem. However, it is often framed as isolated acts of individual-level bias. Racism in peer review manifests across interconnected societal, system, and process levels, shaping who participates in knowledge production, what is deemed credible evidence, and whose work is published. This Indigenous-led Perspective examines racism in peer review through a three-layer framework: (1) societal, (2) system, and (3) process (decision-making and interpersonal), situating peer review as one domain within the broader problem of anti-Indigenous publication bias. Grounded in the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP), Indigenous Data Sovereignty, and lived experience, we integrate conceptual analysis with illustrative examples to identify intervention points. At the societal level, colonial structures and socio-political forces determine whose knowledges count. At the system level, editorial governance, publishing models, and reward structures reproduce inequities. At the process (decision-making and interpersonal) level, racism cuts across reviewer selection and feedback, editorial decision-making, and dissemination through racialized assumptions, assessment and evaluative practices. We propose concrete, systemic reforms to dismantle racism in peer review, including structural policy reform, diversification of reviewer and editorial pools, anti-racism and cultural safety training, and explicit guidelines. We also outline (co)author responsibilities to act in solidarity when racism occurs. Equity, justice, and fairness in peer review demand more than goodwill. They require enforceable structural change, accountability, and culturally safe practice. Confronting racism is both an ethical imperative and a prerequisite for research excellence, integrity, and health promotion that serves all communities.
    Keywords:  Indigenous health; academic publishing; epistemic justice; health promotion; peer review; racism
    DOI:  https://doi.org/10.1093/heapro/daag117
  26. J Am Acad Dermatol. 2026 Aug 04. pii: S0190-9622(26)03217-2. [Epub ahead of print]
    From Peer Review To Prompt Review.
    Contributors Hitaishi Mehta, Rahul Mahajan.
      
    Keywords:  Artificial intelligence; large language models; peer review; research integrity; scientific publishing
    DOI:  https://doi.org/10.1016/j.jaad.2026.07.106
  27. J Food Sci. 2026 Aug;91(8): e71349
    Rating Peer Reviews.
    Richard Hartel.
      
    DOI:  https://doi.org/10.1111/1750-3841.71349
  28. J Clin Epidemiol. 2026 Aug 10. pii: S0895-4356(26)00328-8. [Epub ahead of print] 112452
    Data sharing policies, requirements, and support from public and private clinical trial sponsors: a survey on top sponsors of clinical trials in Europe.
    Ka Hin Tai, Giulia Varvarà, Emma Escoffier, Ulrich Mansmann, Nicholas J DeVito, Anna Catharina V Armond, Florian Naudet.
       OBJECTIVE: To map the presence, public availability, and content of clinical trial data sharing policies, data management and sharing plans, and data use agreements among the most prolific public and private clinical trial sponsors operating in Europe.
    STUDY DESIGN AND SETTING: We included organisation-level documents describing approaches to clinical trial data sharing or data management from the top 20 public and top 20 private sponsors ranked by the number of trials registered in the EU Clinical Trials Information System. Eligible materials comprised publicly available or sponsor-shared policies, guidelines, statements, templates, and agreements relevant to clinical trial data sharing or management. Evidence was identified through systematic searches of sponsors' public websites, structured Google searches, and major data management plan platforms, complemented by direct contact with sponsors to verify findings and request missing documentation. All sources were archived and catalogued. Two reviewers independently extracted data using a structured form, capturing the existence, accessibility, and content of data sharing policies, data management and sharing plans, and data use agreements. Quantitative data were summarised descriptively, and a non-interpretive descriptive content analysis was conducted to characterise recurring policy elements and areas of heterogeneity.
    RESULTS: Among 40 sponsors, private sponsors were substantially more likely than public sponsors to make trial-specific data sharing policies and data use agreements publicly accessible, often via established data sharing platforms. Public sponsors more frequently referenced data management and sharing plans, but these were heterogeneous in scope and often embedded within broader institutional governance documents rather than tailored to clinical trials. Across sectors, General Data Protection Regulation compliance, data protection, and legal safeguards were emphasised, while operational aspects such as dataset readiness, review criteria, and downstream responsibilities varied widely. Overall response rate to sponsor verification was 37.5%.
    CONCLUSION: Clinical trial data sharing governance in the EU shows a marked sectoral imbalance among the top sponsors. Private sponsors tend to provide more detailed and operationally explicit documentation, whereas public sponsors often articulate high-level commitments without trial-specific guidance. Greater clarity and standardisation, particularly among public sponsors, could improve transparency and facilitate responsible data reuse, while remaining compatible with GDPR requirements.
    Keywords:  Clinical trial; Data management; Data sharing; Data use agreements; Individual participant data; Trial funders
    DOI:  https://doi.org/10.1016/j.jclinepi.2026.112452
  29. Korean J Radiol. 2026 Aug 04.
    Reporting Quality of Large Language Model Studies: A Cross-Sectional Audit of High-Ranking Radiology and Medical Imaging Journals.
    Ismail Mese, Saime Turgut Gunes, Ozge Coskun, Ali Keles, Tuba Banaz, Muhammed Faruk Kazanbas, Burak Kocak.
       OBJECTIVE: To evaluate adherence to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) in radiology and medical imaging studies involving large language models (LLMs).
    MATERIALS AND METHODS: We conducted a cross-sectional audit of original LLM research studies published between January 1 and December 26, 2025, in Q1 journals within the Web of Science "Radiology, Nuclear Medicine, and Medical Imaging" category. PubMed and Scopus were searched to identify eligible studies. A quota-based subsampling strategy, based on journal publication volume, was used to select approximately 100 studies. All four eligible articles from the Korean Journal of Radiology (KJR) were additionally included as a benchmark. Adherence to the 2025 update of MI-CLEAR-LLM was scored through a two-round, consensus-based process: an initial assessment by one reviewer followed by a critical re-evaluation by secondary reviewers, with consensus adjudication by an additional reviewer when needed. Between-journal differences were analyzed with the Kruskal-Wallis test, followed by Dunn post hoc pairwise comparisons with Holm-adjusted P-values.
    RESULTS: Of 201 eligible studies identified, 102 were finally analyzed after applying the subsampling strategy. Overall adherence to MI-CLEAR-LLM was moderate (mean, 51.2% ± 14.7%; range, 22.2%-84.2%). Adherence was highest for input data type (100%), test-data independence (80.2%), and adaptation strategy (78.1%), and lowest for prompt execution setup (29.4%) and stochasticity management (33.1%). The least frequently reported items were training-data cutoff date (9.8%) and rationale for prompt wording (15.6%). Adherence varied significantly across journals (P = 0.011), with KJR showing the highest mean adherence (72.8% ± 2.7%).
    CONCLUSION: Reporting transparency in radiology and medical imaging LLM studies published in 2025 was inconsistent across reporting items and journals, with substantial deficiencies in some reproducibility-critical elements. Broader adoption of reporting standards is essential to improve the reproducibility and interpretability of future accuracy evaluations.
    Keywords:  Artificial intelligence; Checklist; Cross-sectional study; Natural language processing; Radiology; Reporting guideline; Reproducibility
    DOI:  https://doi.org/10.3348/kjr.2026.0505
  30. BJOG. 2026 Aug 11.
    Concern About the Trends of Letters to Editors.
    Shigeki Matsubara.
      
    Keywords:  AI detector; artificial intelligence; letter; manuscript
    DOI:  https://doi.org/10.1111/1471-0528.70312
  31. Can J Anaesth. 2026 Aug 12.
    Closing the gap between systematic review protocols and publication in anesthesiology: shared responsibility for transparency and accountability.
    Evelina Pankiv, Kazuyoshi Aoyama.
      
    DOI:  https://doi.org/10.1007/s12630-026-03167-7
  32. Sports Med Int Open. 2026 ;10 a29170363
    Beyond the p -value: Pitfalls in Reporting and Applying Statistical Tests in Sports Science.
    Roberto C M Leite, Rhaí A Arriel, Rodney Paixão, Leandro Sant'Ana, Moacir Marocolo.
      Statistical inference underpins the credibility of sports science research, yet concerns remain regarding the rigor of statistical reporting and analysis. This study assessed the adequacy of statistical practices in sports science research according to established methodological guidelines. A total of 167 original studies published in 2020 across 15 sports science journals indexed in the Journal Citation Reports (Q1-Q3) were systematically reviewed. Up to 50 studies per journal were randomly selected, and data regarding study characteristics and statistical methods were extracted. Statistical adequacy was classified according to predefined criteria involving reporting omissions and/or statistical misapplications. Overall, 87.4% of studies presented at least one inadequacy. The most frequent reporting omissions involved failure to report assumptions such as normality, homogeneity of variances, and sphericity when required. Common statistical misapplications included the use of inappropriate dependent variables and incorrect treatment of independent observations as dependent measures. Deficiencies were identified across all journal quartiles, with no association between adequacy and journal ranking ( p =0.586). These findings raise concerns regarding validity, reproducibility, and practical interpretation in sports science research, reinforcing the need for greater statistical literacy, stricter reporting standards, and improved peer review practices.
    Keywords:  assumption testing; methodological rigor; parametric tests; research methodology; scientific rigor; statistical analysis
    DOI:  https://doi.org/10.1055/a-2917-0363
  33. Aust N Z J Public Health. 2026 Aug 14. pii: S1326-0200(26)00281-5. [Epub ahead of print]50(4): 100592
    Australian and New Zealand introduces the CONSIDER Statement as part of its peer review process.
    Summer May Finlay.
      
    DOI:  https://doi.org/10.1016/j.anzjph.2026.100592
  34. JBI Evid Synth. 2026 Aug 01. 24(8): 1507-1509
    The desk review: practical considerations for authors from the editors' perspective.
    Hien Thi Ho, Tara Crandon.
      
    DOI:  https://doi.org/10.11124/JBIES-26-00298
  35. Can J Anaesth. 2026 Aug 12.
    Deviations between registered protocols and published systematic reviews in high-impact anesthesiology and pain medicine journals: a cross-sectional analysis.
    Alessandro De Cassai, Burhan Dost, Nicole Asti, Angela Bianco, Maria Bisi, Elena Curmaci, Tommaso Antonio Giacon, Marco Nardelli, Serkan Tulgar.
       PURPOSE: Protocol registration is essential for preventing selective reporting bias in systematic reviews, yet deviations from registered protocols remain common across medical fields. We sought to evaluate the prevalence of protocol registration and assessed concordance between registered protocols and published systematic reviews in high-impact anesthesiology journals, hypothesizing that undeclared deviations would be prevalent.
    METHODS: We conducted a cross-sectional analysis of systematic reviews published in 2025 in first-quartile anesthesiology journals according to Journal Citation Reports (Clarivate, London, UK). We searched PubMed® in July 2025 to identify eligible studies. Two reviewers independently assessed concordance between registered protocols and published reviews using a standardized 22-item extraction form covering eligibility criteria; the Population, Intervention, Comparison, Outcome, and Study Design (PICOS) framework; search methods; risk assessment tools; and planned analyses. We recorded agreement as binary outcome, with explicit acknowledgement of deviations noted.
    RESULTS: Of 172 identified systematic reviews, we included 114 after excluding nonregistered studies and those with post hoc registration. All 114 (100%) reviews reported at least one protocol deviation, but only 18 (16%) provided justifications. The most frequent deviation was authorship change (74%), resulting in 317 additional authors without acknowledgement. PICOS framework variations occurred in 65% of reviews, with primary outcome changes in 15% (disclosed in only two cases). Search strategy deviations occurred in 28% of studies, and 38% added Grading of Recommendations Assessment, Development and Evaluation (GRADE) assessments without prior registration.
    CONCLUSIONS: Our study identified at least one deviation from the registered protocol in all of the 114 included systematic reviews published in high-impact anesthesiology journals. Universal protocol deviations with minimal disclosure threaten the integrity of systematic reviews in anesthesiology and pain medicine. These findings show an urgent need for stricter protocol adherence and transparent reporting of deviations by authors, with enhanced oversight by peer reviewers and journal editors.
    STUDY REGISTRATION: Open Science Framework ( https://osf.io/ksezg ); first submitted 10 July 2025.
    Keywords:  anesthesiology; meta-analysis; publication bias; research design; study protocol registration; systematic review
    DOI:  https://doi.org/10.1007/s12630-026-03168-6
  36. Science. 2026 Aug 13. 393(6812): 738
    Writing the hard way.
    Bastien Le Guellec.
      
    DOI:  https://doi.org/10.1126/science.ael3844
  37. J Neurotrauma. 2026 Aug 08. 8977151261475261
    The Open Data Commons for Traumatic Brain Injury: An Interagency Community Platform for Pre-Clinical Data Management and Sharing.
    PRECISE-TBI Investigators
      The Open Data Commons for Traumatic Brain Injury (ODC-TBI.org) was launched in 2018 to support data sharing in pre-clinical TBI. As data science and artificial intelligence continue to advance, open sharing of high-quality, FAIR (Findable, Accessible, Interoperable, and Reusable) data has assumed critical importance to propel discovery science and as a countermeasure to some of the rigor and reproducibility problems plaguing translational research across biomedicine. Researcher-led specialist repositories such as ODC-TBI serve as important hubs through which biomedical communities come together to define data sharing requirements for their respective domains in support of new requirements by funders and journals for routine data sharing. ODC-TBI is now the recognized data repository for pre-clinical TBI research, listed on the National Library of Medicine-recommended repository listing, and is supported by the National Institute on Neurological Disorders and Stroke. ODC-TBI forms one of the critical infrastructure components of the PRE Clinical Interagency reSearch resourcE-TBI (PRECISE-TBI) project, an interagency effort to promote and support data sharing, rigor, and reproducibility in pre-clinical TBI research. Through PRECISE-TBI, the ODC-TBI has conducted broad outreach, starting in 2022, resulting in a significant increase in the number of users, datasets uploaded and public data releases. PRECISE-TBI has facilitated the establishment of an Editorial Board providing community oversight of ODC-TBI policies and recommendations, for example, the use of standards such as common data elements (CDEs). Here we describe the current state of the ODC-TBI, including its organization, operation, and governance. We perform a detailed overview of public datasets to provide insight into data sharing practices, including the use of CDEs and ancillary practices such as providing links to publications and citing data. We examine the impact of the ODC-TBI by providing statistics on downloads per datasets and reuse of ODC-TBI data in published studies. The results not only provide insight into the growth trajectory of ODC-TBI and data sharing behaviors in pre-clinical TBI, but also point to areas where increased outreach, communication, and training are needed to firmly establish a culture of data sharing.
    Keywords:  FAIR principles; animal studies; data sharing; guidelines; injury models
    DOI:  https://doi.org/10.1177/08977151261475261
  38. J Evid Based Med. 2026 Aug 13. e70174
    Inadequate Reporting of Harms in RCTs of Chinese Herbal Medicine Formulas: A Systematic Survey of Leading Medical Journals.
    Rui Zheng, Xiaowei Zhang, Yinghui Jin, Xinran Chen, Yaoyuan Yang, Hongcai Shang, Mitchell Levine.
       OBJECTIVE: To evaluate harms reporting practices in placebo-controlled randomized clinical trials (RCTs) of Chinese herbal medicine (CHM) formulas published in Quartile 1 (Q1) English-language and Tier-1 Chinese journals.
    METHODS: This systematic survey evaluated harms reporting in CHM formula RCTs. We systematically identified eligible RCTs published in English-language journals Q1 (2024 Journal Citation Reports) and Chinese Tier-1 journals (2023 Traditional Chinese Medicine ranking). Two reviewers independently evaluated harms reporting using items derived from the CONSORT Extension for Harms (CONSORT Harms) recommendations and CHM formula-specific reporting elements.
    RESULTS: Among 96 eligible RCTs (49 English Q1; 47 Chinese Tier-1), only 19.8% (n = 19) of trials had a published protocol, with a significantly higher proportion in English Q1 journals (p < 0.001). Only one trial explicitly reported adherence to the CONSORT Harms extension (2004 or 2022). Syndrome differentiation was significantly more frequent in Chinese Tier-1 journals than in English Q1 journals (76.6% vs. 38.8%, p < 0.001). Sixty-seven percent of studies (n = 64) did not report methods for assessing the relationship between harms and interventions, and 15.6% (n = 15) relied solely on clinical judgment. Eighty percent of studies (n = 77) did not report statistical methods for harms analysis. In the results section, 62% (n = 59) did not present harms data in tables.
    CONCLUSIONS: Harms reporting in CHM formula RCTs remains inadequate. Routine adoption of the CONSORT Harms framework, together with CHM formula-specific harms reporting elements, is needed to improve transparency and facilitate the interpretation of harms data in CHM formula research.
    Keywords:  CONSORT Harms; Chinese herbal medicine formula; harms reporting; randomized controlled trials; reporting quality; syndrome differentiation
    DOI:  https://doi.org/10.1111/jebm.70174
  39. Clin Case Rep. 2026 Aug;14(8): e73298
    Writing a Case Report or Case Series: Practical Guidance for Clinicians and Researchers.
    Thomas Aaron Ricks, Roberto Berebichez-Fridman.
      
    DOI:  https://doi.org/10.1002/ccr3.73298
  40. J Adv Nurs. 2026 Aug 10.
    Writing Difference: Deficit Discourse and Epistemic Responsibility in Nursing Scholarship.
    Brendan McCormack, Tamara Power, Debra Jackson.
      
    DOI:  https://doi.org/10.1111/jan.70719
  41. J Nurs Scholarsh. 2026 Sep;58(5): e70128
    Evaluating Evidence-Based Practice and Quality Improvement Initiatives in the Nursing Literature Using a Critical Appraisal Tool.
    Julee Briscoe Waldrop, Staci Reynolds, Jayne Jennings Dunlap.
       INTRODUCTION: Reports of evidence-based practice (EBP) and quality improvement (QI) in nursing journals often vary in reporting quality, hindering translation into practice. A critical appraisal tool for evidence-based practice quality improvement (EBPQI) has recently been made available on the EQUATOR Network.
    METHODS: A descriptive cross-sectional study of full-length articles (Jan-Jun 2025) from 28 nursing journals that accept reports of EBP and/or QI (per submission guidelines); each article was scored by two authors using the open-access critical appraisal tool for EBPQI.
    RESULTS: Collectively, these journals published 86 issues and 949 full-length articles; 30 (3.2%) articles were identified as EBP or QI reports and were published among 10 (33%) journals. Of these 30 articles, only 6 (20%) used appropriate EBP and/or QI methods and were therefore appraised using the EBPQI critical appraisal tool. Based on the EBPQI critical appraisal tool, the EBP and QI reports varied in how and to what extent they met EBPQI criteria.
    CONCLUSION: Variation exists in journal guidance and reporting of EBP/QI initiatives. Consistent use of EBPQI criteria highlights the need for standardized reporting to improve transparency, transferability, and translation into practice.
    CLINICAL RELEVANCE: Transparent, standardized reporting of EBP and/or QI initiatives enables clinicians to appraise relevance and transferability to practice. Reporting aligned with recognized guidelines (or clear justification for deviations) is warranted.
    Keywords:  EBP; EBPQI; QI; critical appraisal; evidence‐based practice quality improvement; mountain model
    DOI:  https://doi.org/10.1111/jnu.70128
  42. BMJ. 2026 Aug 11. 394 e100438
    Pitfalls of announcing regulatory policy in medical journals.
    S Sean Tu, Aaron S Kesselheim.
      
    DOI:  https://doi.org/10.1136/bmj-2026-100438
  43. J Med Imaging (Bellingham). 2026 Jul;13(4): 040101
    Strengthening the JMI-SPIE Medical Imaging Connection.
    Bennett A Landman, Ivana Išgum.
      The editorial announces two pilot initiatives linking SPIE Medical Imaging and the Journal of Medical Imaging: a journal-first conference presentation pathway for recently published JMI papers and a streamlined route for selected conference papers to progress to journal publication while preserving rigorous peer review.
    DOI:  https://doi.org/10.1117/1.JMI.13.4.040101
  44. Turk J Haematol. 2026 08 13.
    75 Years of Adventure in Turkish Scientific Medical Publishing: From Istanbul Contribution to Clinical Science to Turkish Journal of Hematology (1951-2026).
    Ahmet Muzaffer Demir.
      
    DOI:  https://doi.org/10.4274/tjh.galenos.2026.79346
  45. J Shoulder Elbow Surg. 2026 Aug 01. pii: S1058-2746(26)00464-7. [Epub ahead of print]
    Introduction of a Journal of Shoulder and Elbow Surgery manuscript processing fee.
    John E Jed Kuhn, William J Mallon.
      
    DOI:  https://doi.org/10.1016/j.jse.2026.07.019
  46. Exp Hematol. 2026 Aug 12. pii: S0301-472X(26)00125-6. [Epub ahead of print] 105492
    Share Your Research in Experimental Hematology-Announcing Exciting New Article Types!
    Keisuke Ito.
      
    DOI:  https://doi.org/10.1016/j.exphem.2026.105492
  47. Indian J Crit Care Med. 2026 Jul;30(7): 547
    Making an Impact with the Indian Journal of Critical Care Medicine.
    Jigeeshu V Divatia.
      
    Keywords:  Critical Care; Indian Journal of Critical Care Medicine; Journal impact factor; Patient care; Peer review; Social media
    DOI:  https://doi.org/10.5005/jp-journals-10071-25251
  48. J Spine Surg. 2026 Jul 31. 12(7): 108
    A decade of impact: the Journal of Spine Surgery enters its next phase.
    Ralph J Mobbs.
      
    Keywords:  Spine surgery; academic publishing; endoscopic spine surgery; journal impact; scientific communication
    DOI:  https://doi.org/10.21037/jss-20262-04
  49. J Neuroimmunol. 2026 Jul 30. pii: S0165-5728(26)00191-8. [Epub ahead of print] 579042
    Generative artificial intelligence in manuscript writing and peer review for the Journal of Neuroimmunology.
    Gregory F Wu, Robyn S Klein, Ashutosh K Mangalam, Kristen E Funk, Hanane Touil, Qiang Liu, Ralf Gold, Laura Piccio.
      
    DOI:  https://doi.org/10.1016/j.jneuroim.2026.579042
  50. J Bacteriol. 2026 Aug 14. e0034026
    A journal that grows older and stays young.
    Tao Dong.
      
    Keywords:  China; bacteriology; education; history
    DOI:  https://doi.org/10.1128/jb.00340-26
  51. J Nutr Educ Behav. 2026 Aug;pii: S1499-4046(26)00405-7. [Epub ahead of print]58(8): 720
    Reflections on Two Years as Mentored Editor of the Journal of Nutrition Education and Behavior.
    Lynn Samson Brann.
      
    DOI:  https://doi.org/10.1016/j.jneb.2026.07.002
  52. J Korean Neurosurg Soc. 2026 Aug 07.
    Where Does JKNS Stand in JCR 2025, and How Should We Proceed for Sustainable Growth?
    Hee-Jin Yang.
      The 2025 Journal Citation Reports (JCR) reveal that the Impact Factor (IF) for the Journal of Korean Neurosurgical Society (JKNS) has risen to 1.9, an increase from 1.7 in 2024. The upward trend in the IF compared to previous years is a highly encouraging development. Although the IF of JKNS has increased, a substantial gap still remains compared to top-ranked neurosurgical journals. The time has come to set clear goals for where JKNS should head next and to explore how to reach them. While maintaining our existing strategies, we aim to consider actionable approaches to advance to the next level.
    Keywords:  Future development; Impact factor; Journal of Korean Neurosurgical Society
    DOI:  https://doi.org/10.3340/jkns.2026.0223
  53. Health (London). 2026 Aug 09. 13634593261477586
    Editorial: 30 Years of Health.
    Rebecca E Olson, Chris Till.
      
    DOI:  https://doi.org/10.1177/13634593261477586
  54. J Midlife Health. 2026 Apr-Jun;17(2):17(2): 199-200
    Journey of the Journal of Mid-Life Health - Continuing the Legacy.
    Seema Sharma, Reeti Mehra.
      
    DOI:  https://doi.org/10.4103/jmh.jmh_192_26
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