Quantitative evidence synthesis method has become a central tool for integration of findings across multiple studies, multi-centre trials, and multi-source cohort data. However, the identification and interpretation of non-replicable, outlying, and influential studies remain insufficiently addressed in practice, despite their potential to substantially affect the robustness and credibility of meta-analytic conclusions. In this paper, we clarify the conceptual distinctions between non-replicability, statistical outlyingness, and study influence, emphasizing that these concepts are related but not interchangeable. We then review the standard principles and procedures of model diagnostics for detecting outlying and influential studies in meta-analysis, together with their underlying statistical rationale. Building on recent methodological developments, we further discuss several practical and methodological refinements, including approaches for handling imprecise and correlated sampling variances, robust diagnostic procedures, and graphical tools for facilitating the identification and interpretation of unusual studies. Finally, we summarize recent advances in outlier and influence diagnostics and provide recommendations for the cautious interpretation and evaluation of studies identified as potentially non-replicable, outlying, or influential within meta-analytic frameworks.

翻译：定量证据综合方法已成为整合多项研究、多中心试验及多源队列数据发现的核心工具。然而，尽管不可复制、异常及有影响的研究可能显著影响元分析结论的稳健性与可信度，实践中对其识别与解释仍缺乏充分探讨。本文首先厘清不可复制性、统计异常性与研究影响力之间的概念差异，强调这些概念虽互相关联但不可相互替代。继而系统回顾元分析中检测异常与有影响研究的标准模型诊断原则与流程及其统计学依据。基于最新方法论进展，进一步讨论若干实用及方法学改进措施，包括处理不精确与相关抽样方差的策略、稳健诊断程序，以及促进异常研究识别与解释的可视化工具。最后，总结离群值与影响诊断领域的最新进展，并就如何在元分析框架内审慎解释与评估可能具有不可复制性、异常性或影响力的研究提出建议。