用自动化流程高效整合子宫内膜异位症复发研究，结果可靠可复现。
Computational-Assisted Systematic Review and Meta-Analysis (CASMA): Effect of a Subclass of GnRH-a on Endometriosis Recurrence
- 结合文献筛选指南与模糊匹配技术，半自动去重并过滤3.3万条记录
- 7项随机对照试验显示复发风险降低36%，异质性极低（I²=0.00%）
- 适合医学研究者和计算科学交叉团队参考，提升循证医学效率
背景：证据整合推动循证医学发展，但医学文献爆炸式增长使传统方法难以应对。目的：评估一种基于信息检索的工作流CASMA，以提升系统评价的效率、透明度和可复现性。子宫内膜异位症复发因文献复杂且模糊，成为理想案例。方法：采用混合方法，融合PRISMA指南与模糊匹配、正则表达式（regex），实现半自动化去重与初筛。工作流整合了关于促性腺激素释放激素激动剂（GnRH-a）亚类在随机对照试验中的疗效证据。通过改进的分组方法纠正多臂试验的单位分析错误。结果：工作流大幅减少筛选工作量，仅用11天完成33,444条记录的获取与过滤。共纳入7项合格随机对照试验（841名患者）。随机效应模型显示风险比（RR）为0.64（95% CI 0.48–0.86），表明复发率降低36%，异质性不显著（I²=0.00%，τ²=0.00）。敏感性分析验证结果稳健。结论：该研究展示了信息检索驱动工作流在医学证据整合中的应用。方法产出有价值临床结果，并提供可扩展框架，弥合临床研究与计算机科学之间的差距。
原文摘要 · Abstract (English)
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 ($I^2=0.00\%$, $τ^2=0.00$). 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.
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