Background: Evidence synthesis facilitates evidence-based medicine. This task becomes increasingly difficult to accomplished with applying computational solutions, since the medical literature grows at astonishing rates. Objective: This study evaluates an information retrieval-driven workflow, CASMA, to enhance the efficiency, transparency, and reproducibility of systematic reviews. Endometriosis recurrence serves as the ideal case due to its complex and ambiguous literature. Methods: The hybrid approach integrates PRISMA guidelines with fuzzy matching and regular expression (regex) to facilitate semi-automated deduplication and filtered records before manual screening. The workflow synthesised evidence from randomised controlled trials on the efficacy of a subclass of gonadotropin-releasing hormone agonists (GnRH-a). A modified splitting method addressed unit-of-analysis errors in multi-arm trials. Results: The workflow sharply reduced the screening workload, taking only 11 days to fetch and filter 33,444 records. Seven eligible RCTs were synthesized (841 patients). The pooled random-effects model yielded a Risk Ratio (RR) of $0.64$ ($95\%$ CI $0.48$ to $0.86$), demonstrating a $36\%$ reduction in recurrence, with non-significant heterogeneity ($I^2=0.00\%$, $\tau^2=0.00$). The findings were robust and stable, as they were backed by sensitivity analyses. Conclusion: This study demonstrates an application of an information-retrieval-driven workflow for medical evidence synthesis. The approach yields valuable clinical results and a generalisable framework to scale up the evidence synthesis, bridging the gap between clinical research and computer science.

翻译：背景：证据综合是循证医学的基础。随着医学文献的惊人增长，不借助计算解决方案，这一任务正变得日益难以完成。目的：本研究评估一种信息检索驱动的工作流程——CASMA，旨在提升系统评价的效率、透明度与可重复性。子宫内膜异位症复发因其文献复杂且存在歧义，成为理想的验证案例。方法：该混合方法将PRISMA指南与模糊匹配及正则表达式（regex）相结合，以促进半自动去重，并在人工筛选前完成记录过滤。该工作流程综合了关于促性腺激素释放激素激动剂（GnRH-a）某一亚类疗效的随机对照试验证据。采用一种改进的分组方法处理多臂试验中的分析单位错误。结果：该工作流程显著减少了筛选工作量，仅用11天即获取并过滤了33,444条记录。共综合了7项符合条件的RCT（841名患者）。汇总随机效应模型得出的风险比（RR）为$0.64$（$95\%$ CI $0.48$至$0.86$），表明复发风险降低了$36\%$，且异质性不显著（$I^2=0.00\%$，$\tau^2=0.00$）。敏感性分析支持了研究结果的稳健性与稳定性。结论：本研究展示了信息检索驱动的工作流程在医学证据综合中的应用。该方法不仅得出了有价值的临床结果，还提供了一个可推广的框架以扩大证据综合的规模，从而弥合临床研究与计算机科学之间的鸿沟。