Increased endorsement of TRIPOD and other reporting guidelines by high impact factor journals: survey of instructions to authors

Exploring the integration of medical and preventive chronic disease health management in the context of big data

Chronic non-communicable diseases (NCDs) pose a significant global health burden, exacerbated by aging populations and fragmented healthcare systems. This study employs a comprehensive literature review method to systematically evaluate the integration of medical and preventive services for chronic disease management in the context of big data, focusing on pre-hospital risk prediction, in-hospital clinical prevention, and post-hospital follow-up optimization. Through synthesizing existing research, we propose a novel framework that includes the development of machine learning models and interoperable health information platforms for real-time data sharing. The analysis reveals significant regional disparities in implementation efficacy, with developed eastern regions demonstrating advanced closed-loop management via unified platforms, while western rural areas struggle with manual workflows and data fragmentation. The integration of explainable AI (XAI) and blockchain-secured care pathways enhances clinical decision-making while ensuring GDPR-compliant data governance. The study advocates for phased implementation strategies prioritizing data standardization, federated learning architectures, and community-based health literacy programs to bridge existing disparities. Results show a 30-35% reduction in redundant diagnostics and a 15-20% risk mitigation for cardiometabolic disorders through precision interventions, providing a scalable roadmap for resilient public health systems aligned with the "Healthy China" initiative.

Assessment of Standards for Reporting of Diagnostic Accuracy (STARD) 2015 guideline adherence in medical imaging diagnostic accuracy studies published in 2023

BACKGROUND

The Standards for Reporting of Diagnostic Accuracy (STARD) 2015 guideline facilitates evaluation of key aspects of diagnostic test accuracy (DTA) studies and their findings, including the risk of bias and applicability of findings.

OBJECTIVE

To evaluate the completeness of reporting in medical imaging DTA research in a sample of studies published in 2023.

METHODS

A systematic search of Medline, Embase, and the Cochrane Library was performed to identify medical imaging DTA studies published between January and June 2023 that assessed one or more index imaging tests compared to a reference standard and reported test performance using relevant outcome measures. Completeness of reporting amongst the included studies was assessed using the 30-item STARD-2015 guideline. Multiple linear regression was subsequently performed to identify study characteristics associated with more complete reporting.

RESULTS

A total of 116 studies were included in our analysis with a median journal impact factor of 2.7 (range 0.9-19.7). The mean number of items reported was 17.5/30 (58%, SD 2.2). Items that were infrequently reported (reported in less than 33% of included studies) included items 9 ('whether participants formed a consecutive, random or convenience series'), 13.2 ('whether clinical information and index test results were available to the assessors of the reference standard'), 15 ('how indeterminate index test or reference standard results were handled'), 16 ('how missing data on the index test and reference standard were handled'), 22.1 ('time interval between the index test and the reference standard'), 22.2 ('clinical interventions between the index test and the reference standard') and 29 ('where the full study protocol can be accessed'). Adherence was significantly higher in journals with a higher than median journal impact factor (18.1/30 vs 16.8/30 items reported; P < .001).

CONCLUSION

The completeness of reporting in medical imaging DTA research is moderate and remains relatively static in absolute terms compared to a previous evaluation of studies published in 2016 performed by Hong and colleagues, acknowledging differences in sample study characteristics limit direct comparison. Potential strategies to support more complete reporting in medical imaging DTA research include mandating adherence to the STARD guideline in journal instructions to authors, requiring completed STARD checklists to be submitted alongside all DTA study manuscripts, and integrating quality of reporting assessment as a routine component of the peer review process.

The policies on the use of large language models in radiological journals are lacking: a meta-research study

OBJECTIVE

To evaluate whether and how the radiological journals present their policies on the use of large language models (LLMs), and identify the journal characteristic variables that are associated with the presence.

METHODS

In this meta-research study, we screened Journals from the Radiology, Nuclear Medicine and Medical Imaging Category, 2022 Journal Citation Reports, excluding journals in non-English languages and relevant documents unavailable. We assessed their LLM use policies: (1) whether the policy is present; (2) whether the policy for the authors, the reviewers, and the editors is present; and (3) whether the policy asks the author to report the usage of LLMs, the name of LLMs, the section that used LLMs, the role of LLMs, the verification of LLMs, and the potential influence of LLMs. The association between the presence of policies and journal characteristic variables was evaluated.

RESULTS

The LLM use policies were presented in 43.9% (83/189) of journals, and those for the authors, the reviewers, and the editor were presented in 43.4% (82/189), 29.6% (56/189) and 25.9% (49/189) of journals, respectively. Many journals mentioned the aspects of the usage (43.4%, 82/189), the name (34.9%, 66/189), the verification (33.3%, 63/189), and the role (31.7%, 60/189) of LLMs, while the potential influence of LLMs (4.2%, 8/189), and the section that used LLMs (1.6%, 3/189) were seldomly touched. The publisher is related to the presence of LLM use policies (p < 0.001).

CONCLUSION

The presence of LLM use policies is suboptimal in radiological journals. A reporting guideline is encouraged to facilitate reporting quality and transparency.

CRITICAL RELEVANCE STATEMENT

It may facilitate the quality and transparency of the use of LLMs in scientific writing if a shared complete reporting guideline is developed by stakeholders and then endorsed by journals.

KEY POINTS

The policies on LLM use in radiological journals are unexplored. Some of the radiological journals presented policies on LLM use. A shared complete reporting guideline for LLM use is desired.