Objective Based on the methodology of evidence-based medicine, we explored the prognosis of a patient with gestational diabetes mellitus (GDM). Methods We searched ACP Journal Club (1991 to October 2006), The Cochrane Library (Issue 4, 2006), MEDLINE (1990 to October 2006) and Chinese Biomedicine database (CBM). Cohort studies, case-control studies and case series studies involving the prognosis of patients with GDM were collected. The available evidence was critically appraised. Results During the period from 6 weeks to 28 years after delivery, the incidence of type 2 diabetes mellitus appeared to vary from 2.6% to 70%. Patients with GDM suffered from an increased incidence of spontaneous premature delivery, hypertension, metabolic syndrome and vaginal infection. Conclusion Patients with GDM appears to be more liable to overt diabetes mellitus, and to suffer fromspontaneous premature delivery, hypertension, metabolic syndrome and vaginal infection than women with normal glucose tolerance during pregnancy. Further studies of the long-term follow-up data from GDM trials are needed.
Objective To explore the causal association between obstructive sleep apnea (OSA) and venous thromboembolism (VTE). Methods Using the summary statistical data from the FinnGen biological sample library and IEU OpenGWAS database, the relationship between OSA and VTE, including deep vein thrombosis (DVT) and pulmonary embolism, was explored through Mendelian randomization (MR) method, with inverse variance weighted (IVW) as the main analysis method. Results The results of univariate MR analysis using IVW method showed that OSA was associated with VTE and pulmonary embolism (P<0.05), with odds ratios and 95% confidence intervals of 1.204 (1.067, 1.351) and 1.352 (1.179, 1.544), respectively. There was no correlation with DVT (P>0.05). Multivariate MR analysis showed that after adjustment for confounding factors (smoking, diabetes, obesity and cancer), OSA was associated with VTE, DVT and pulmonary embolism (P<0.05), with odds ratios and 95% confidence intervals of 1.168 (1.053, 1.322), 1.247 (1.064, 1.491) and 1.158 (1.021, 1.326), respectively. Conclusion OSA increases the risk of VTE, DVT, and pulmonary embolism.
As artificial intelligence (AI) is rapidly integrated into evidence synthesis workflows—spanning literature retrieval and deduplication, screening, risk-of-bias assessment, data extraction and structuring, and drafting and updating evidence products. While AI may improve efficiency, it can also compromise methodological rigor through non-reproducible outputs, systematic false exclusions, distorted bias judgments, extraction errors, and inappropriate summarization. Ensuring traceability, accountability, and auditability while leveraging efficiency gains has therefore become a central challenge for the evidence synthesis community. In November 2025, four leading evidence organizations—Cochrane, the Campbell Collaboration, the Joanna Briggs Institute (JBI), and the Collaboration for Environmental Evidence (CEE)—issued a joint statement on the responsible use of AI in evidence synthesis and endorsed the responsible use of AI in evidence synthesis (RAISE) framework. The joint statement articulates core positions, including human accountability, transparent disclosure, and human oversight, whereas RAISE operationalizes these principles through actionable recommendations covering roles and responsibilities within the evidence ecosystem, requirements for building and evaluating AI tools, and ethical considerations for selecting and using AI in specific projects. This paper interprets the key elements of the joint statement and the RAISE framework, presents a scenario-based view of risk control in major stages of evidence synthesis, and discusses practical implications for Chinese-language contexts, including differences in Chinese-language text forms, heterogeneity of traditional Chinese medicine evidence, policy and journal disclosure requirements, and constraints related to cost and accessibility. The analysis aims to provide a structured reference for the responsible, locally applicable use of AI in evidence synthesis in China.