data-extraction

To extract data for a systematic review, this Skill applies Cochrane and JBI templates to capture study characteristics, outcomes, and effect estimates from clinical studies. It guides the process using design-specific considerations for RCTs, cohorts, and qualitative research.

Yes, Cochrane and JBI templates are used here to support data extraction across multiple designs including randomized trials, cohorts, prevalence studies, and qualitative research, ensuring accurate capture of outcomes and time points with design-specific considerations.

To distinguish reported data from calculated values like SD derived from SE or CI, this Skill flags uncertainties and applies built-in validation rules during extraction. It ensures clear differentiation between reported and calculated effect estimates for meta-analysis.

The best way to generate analysis-ready datasets for meta-analysis is using template-driven extraction that produces structured datasets with traceable sources. This Skill outputs documentation and data structured specifically for meta-analysis workflows.

Yes, data extraction for systematic reviews includes built-in validation rules and traceable sources for every captured outcome and effect estimate. This ensures data integrity and transparency throughout the extraction process.

Structured templates for systematic review data extraction require accurate source differentiation between reported and calculated values to avoid errors. While they support multiple designs, users must manually verify flagged uncertainties to maintain data integrity.