قارن الطرق

	مراجعة مظلية متوافقة مع PRISMA	مراجعة الأدبيات المنهجية
المجال	القياسات العلمية	القياسات العلمية
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	2015 (umbrella review methodology); 2022 (PRIOR reporting extension)	1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines)
صاحب الطريقة	Aromataris et al. (JBI); PRIOR reporting extension by Fusar-Poli et al.	Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004)
النوع	Evidence synthesis / review of systematic reviews	Evidence synthesis methodology
المصدر التأسيسي	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 approach. International Journal of Evidence-Based Healthcare, 13(3), 132–140.	Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401.
الأسماء البديلة	umbrella review with PRISMA, PRIOR-guided umbrella review, overview of reviews, PRISMA umbrella overview	SLR, systematic review, evidence synthesis review, structured literature review
ذات صلة	5	5
الملخص	A PRISMA-compliant umbrella review is a structured synthesis of existing systematic reviews and meta-analyses on a topic, conducted and reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines — specifically the PRIOR extension developed for umbrella reviews. By operating one level above primary research, it maps the totality of evidence, identifies convergent or contradictory findings across reviews, and quantifies evidence strength at the highest tier of the evidence hierarchy.	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.