Generalisability and transportability of clinical prediction models (CPMs) refer to their ability to maintain predictive performance when applied to new populations. While CPMs may show good generalisability or transportability to a specific new population, it is rare for a CPM to be developed using methods that prioritise good generalisability or transportability. There is an emerging literature of such techniques; therefore, this scoping review aims to summarise the main methodological approaches, assumptions, advantages, disadvantages and future development of methodology aiding the generalisability/transportability. Relevant articles were systematically searched from MEDLINE, Embase, medRxiv, arxiv databases until September 2023 using a predefined set of search terms. Extracted information included methodology description, assumptions, applied examples, advantages and disadvantages. The searches found 1,761 articles; 172 were retained for full text screening; 18 were finally included. We categorised the methodologies according to whether they were data-driven or knowledge-driven, and whether are specifically tailored for target population. Data-driven approaches range from data augmentation to ensemble methods and density ratio weighting, while knowledge-driven strategies rely on causal methodology. Future research could focus on comparison of such methodologies on simulated and real datasets to identify their strengths specific applicability, as well as synthesising these approaches for enhancing their practical usefulness.

翻译：临床预测模型（CPMs）的泛化性与可迁移性指其应用于新人群时保持预测性能的能力。虽然CPMs可能对特定新人群表现出良好的泛化性或可迁移性，但现有CPMs很少采用优先考虑良好泛化性或可迁移性的方法进行开发。相关技术的研究文献正在兴起，因此本范围综述旨在总结有助于提升泛化性/可迁移性的主要方法学路径、基本假设、优势局限及未来发展方向。通过预定义的检索策略，系统检索了截至2023年9月MEDLINE、Embase、medRxiv、arxiv数据库中的相关文献。提取信息包括方法描述、假设条件、应用实例、优势与不足。初检获得1761篇文献，172篇进入全文筛选，最终纳入18篇。我们根据方法属于数据驱动或知识驱动、是否针对目标人群专门设计进行了分类。数据驱动方法涵盖数据增强、集成学习到密度比加权等技术，而知识驱动策略则依赖于因果推断方法学。未来研究可聚焦于通过模拟与真实数据集比较这些方法以明确其特定适用优势，并整合多类方法以提升实际应用价值。