BMJ Evidence-Based Medicine最新文献

Background: Misleading information about medical products on social media may cause overuse.

Objectives: Explore interventions targeting the problem of misleading medical information and marketing on social media, with a focus on preventing medical overuse including overdiagnosis.

Eligibility criteria: We included peer-reviewed studies with original data on an intervention targeting misleading medical information on social media and governmental/institutional responses with and without evaluation. We excluded responses relating to COVID-19.

Sources of evidence: four electronic databases: MEDLINE/PubMed, PsycINFO, Academic Search Complete and Web of Science, and searches of grey literature on Google and Google Scholar. Search date: 9 June 2025.

Data charting: We used prespecified data forms populated in duplicate by two reviewers.

Results: We identified 27 peer-reviewed articles and 25 organisational and governmental responses (grey literature). 20 (74%) of the peer-reviewed interventions targeted the consumer to enhance 'media literacy', support decision-making or warn about misinformation trends. Approaches included education, such as videos or information materials, to improve detection of misinformation, as well as correcting misinformation and rebutting claims. Only two (7.4%) of the peer-reviewed approaches were sensitive to the problem of medical overuse: a risk-of-deception tool and an informed decision-making service. The grey literature about government and organisational responses chiefly comprised general advertising regulations and other educational resources for consumers to identify and navigate misinformation. The advertising regulations ranged from self-regulatory codes of practice to mandatory regulations, requiring pre-approval of social media marketing material. Most regulations stated advertising should be truthful, presenting both benefits and harms and not be misleading. Most of the grey literature (64%) was sensitive to medical overuse, though none referred explicitly to the problem.

Conclusions: Current efforts to address misleading medical marketing on social media often overlook the critical issue of medical overuse and fail to provide sufficient consumer protections in this rapidly evolving digital landscape of social media, such as the speed of dissemination, reach and the role of third-party advertising. These gaps in research, regulation and practice present significant opportunities to strengthen evidence-based policies and public health responses. TRIAL REGISTRATION DETAILS: https://doi.org/10.17605/OSF.IO/2NJSH.

Open scholarship practices-often also referred to as open research or open science-are increasingly seen as relevant to the research lifecycle. What open scholarship looks like in practice will inevitably vary by field, methodology, epistemology, access to resources, and other considerations. In this article, we outline a few important considerations for researchers who use qualitative research methods in deciding which open scholarship practices are most applicable for their own work and suggest a few practices that may be of interest, namely data sharing, preregistration, negative case analysis, audit trails and member checking. Engagement or disengagement of any given practice should be guided by demands of research epistemology, context, community, topicn and methodology. Finally, we suggest a few places for researchers to start if they wish to learn more about open scholarship more broadly and engage with critical conversations around these practices.

Objectives: Generative artificial intelligence (GAI) tools can enhance the quality and efficiency of medical research, but their improper use may result in plagiarism, academic fraud and unreliable findings. Transparent reporting of GAI use is essential, yet existing guidelines from journals and institutions are inconsistent, with no standardised principles.

Design and setting: International online Delphi study.

Participants: International experts in medicine and artificial intelligence.

Main outcome measures: The primary outcome measure is the consensus level of the Delphi expert panel on the items of inclusion criteria for GAMER (Rreporting guideline for the use of Generative Artificial intelligence tools in MEdical Research).

Results: The development process included a scoping review, two Delphi rounds and virtual meetings. 51 experts from 26 countries participated in the process (44 in the Delphi survey). The final checklist comprises nine reporting items: general declaration, GAI tool specifications, prompting techniques, tool's role in the study, declaration of new GAI model(s) developed, artificial intelligence-assisted sections in the manuscript, content verification, data privacy and impact on conclusions.

Conclusion: GAMER provides universal and standardised guideline for GAI use in medical research, ensuring transparency, integrity and quality.