手法を比較

	プロトコルに基づくアンブレラレビュー	システマティック・レビュー
分野	科学計量学	科学計量学
系統	Process / pipeline	Process / pipeline
提唱年	2011-2015 (PROSPERO launched 2011; JBI umbrella review guidelines 2015)	1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines)
提唱者	Developed from umbrella review methodology; protocol registration practice formalized through PROSPERO (York) and JBI	Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004)
種類	Registered evidence synthesis review	Evidence synthesis methodology
原典	Aromataris, E., Fernandez, R., Godfrey, C. M., Holly, C., Khalil, H., & Tungpunkom, P. (2015). Summarizing systematic reviews: methodological development, conduct and reporting of an umbrella review. JBI Evidence Implementation, 13(3), 132-140.	Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401.
別名	pre-registered umbrella review, prospero-registered umbrella review, registered overview of reviews, protocol-driven umbrella review	SLR, systematic review, evidence synthesis review, structured literature review
関連	5	5
概要	A protocol-based umbrella review is an umbrella review — a synthesis of existing systematic reviews and meta-analyses on a common topic — conducted under a publicly pre-registered protocol, typically in PROSPERO or a similar registry. Pre-registering the protocol before data collection begins commits the research team to prospectively defined eligibility criteria, search strategy, appraisal tools, and synthesis methods, sharply reducing the risk of outcome reporting bias and post-hoc analytical flexibility.	A systematic literature review (SLR) is a structured, reproducible method for identifying, appraising, and synthesizing all relevant studies on a research question. Unlike a narrative review, it follows an explicit, pre-specified protocol — from database search strings through inclusion criteria to data extraction — so that the process is transparent, auditable, and replicable by other researchers. It is widely used in medicine, education, software engineering, and the social sciences to produce the most comprehensive possible evidence base on a topic.