【摘要】目的探討腹腔鏡聯合盆腔理療對輸卵管性不孕癥的治療效果,旨在提高術后的受孕率。方法將2007年1月2008年12月進行診治的不孕者86例隨機分為干預組與對照組,每組43例。干預組腹腔鏡手術治療,術后進行理療;對照組不進行理療。調查并比較兩組的治療效果和患者滿意度。結果干預組43例中13例再次宮內妊娠,受孕率為30.23%;對照組再次宮內妊娠,8例(18.60%)兩組比較差異具有統計學意義(Plt;0.05)。干預組總有效率86.05%,明顯優于對照組67.44%,且差異亦具有統計學意義(Plt;0.05)。患者滿意率干預組為90.67%(39/43),對照組為76.74%(33/43)。兩組差異具有統計學意義(Plt;0.05)。結論腹腔鏡聯合盆腔理療可以有效改善治療效果,提高再次受孕率,值得在臨床實踐中推廣應用。
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.