Description

This Master Prompt develops a structured framework for transforming included reviews into accurate, traceable and analytically useful evidence. It addresses extraction-form design, data dictionaries, source identification, review characteristics, populations, interventions, exposures, outcomes, effect estimates, study counts, appraisal findings and qualitative concepts. It also covers classification systems, hierarchical data structures, review updates, duplicate reporting, missing information, inconsistent values and links between reviews and their underlying studies. The supplied papers illustrate different extraction needs across quantitative synthesis, prevalence research, exposure assessment, conceptual frameworks and evidence mapping. Particular emphasis is placed on preserving original definitions, analytical units, uncertainty and source provenance. The framework supports transparent comparison and synthesis while preventing double-counting, unjustified data conversion, loss of contextual information and unsupported reconstruction of missing results.