Description

This Master Prompt focuses on systematically transforming the final corpus of included publications into a structured, traceable, internally consistent, and analysis-ready evidence base. It guides researchers in designing a purpose-specific extraction architecture; defining extraction variables and coding rules; piloting and refining the extraction form; recording bibliographic, methodological, theoretical, contextual, population, technological, experimental, analytical, and outcome characteristics; and constructing standardized evidence matrices or review databases. The extraction framework must be driven by the review problem, objectives, questions, review type, and characteristics of the available evidence rather than by a universal template. The uploaded protocol literature illustrates this principle through extraction of author information, publication year and type, population and sample size, location, study design, risk and protective factors, outcomes and key findings, while allowing emerging characteristics to be added through iterative coding and accuracy checks.

A major emphasis is placed on separating evidence extraction from evidence interpretation. Researchers should first record what each source actually reports, where it reports it, in what units, under what conditions, and with what level of completeness before converting the evidence into categories, comparisons, themes, explanations, or conclusions. Depending on the review domain, extraction may encompass theories and conceptual frameworks; populations and samples; interventions, exposures and comparators; technologies, materials and datasets; experimental or operating conditions; analytical or computational methods; outcome definitions and measurement instruments; quantitative estimates; qualitative findings; implementation conditions; limitations; and recommendations. This flexibility is reflected across the uploaded collection: the Knowledge Graph review systematically maps methodologies, tools, datasets, applications, challenges and future directions, whereas the childhood-loss protocol proposes tabular charting of study characteristics and subsequent coding of emerging concepts.