Standardized Review Extraction Dataset (SRED): A Structured Framework for Systematic Review Data Extraction and Evidence Extractability Assessment

Systematic reviews and meta-analyses rely on the accurate, consistent, and transparent extraction of study characteristics and outcome data from primary research reports. Variability in data extraction practices, incomplete reporting of methodological details, and inconsistent documentation of outcome data can introduce important challenges during evidence synthesis, including reduced reproducibility, limitations in quantitative meta-analysis, and difficulties in assessing the completeness of available evidence.

The Standardized Review Extraction Dataset (SRED) framework was developed to provide a structured methodological approach for systematic review data extraction and to support consistent documentation of study information required for evidence synthesis. The framework introduces a standardized architecture for organizing extraction variables and provides a transparent approach for assessing the extractability of reported study data.

The SRED framework organizes extraction variables into ten predefined domains representing core informational components of primary research studies, including:

• bibliographic study identification

• methodological study design characteristics

• participant population characteristics

• intervention or exposure descriptions

• outcome definitions and measurement

• statistical outcome data required for synthesis

• risk-of-bias indicators

• safety and harms reporting

• transparency and reproducibility indicators

• evidence synthesis readiness

To improve extraction consistency and reduce variability between reviewers, each extraction criterion is evaluated using standardized response categories indicating the clarity and extractability of reported information. In addition, extraction criteria are assigned priority weights reflecting their relative importance for quantitative evidence synthesis and interpretation of study findings. 

A central feature of the framework is the Extraction Integrity Score (EIS), a quantitative indicator designed to summarize the completeness and extractability of reported study information required for systematic evidence synthesis. The EIS is calculated as a weighted proportion of extractable study information relative to all applicable extraction criteria, thereby providing a structured metric for evaluating whether study reports contain sufficient information for evidence synthesis. 

By combining standardized extraction domains, response coding, and priority weighting, the SRED framework aims to support:

• improved transparency in systematic review data extraction

• enhanced reproducibility of evidence synthesis workflows

• structured documentation of study characteristics

• systematic evaluation of reporting completeness

• improved readiness of primary studies for quantitative meta-analysis

The SRED framework is designed to complement established methodological guidance, including reporting standards such as PRISMA and methodological recommendations described in the Cochrane Handbook for Systematic Reviews of Interventions. It does not replace risk-of-bias assessment tools or reporting guidelines but instead provides a structured operational framework for documenting and evaluating the extractability of study data during systematic review processes. 

Potential applications of the SRED framework include:

• systematic reviews and meta-analyses

• umbrella reviews and evidence mapping studies

• methodological meta-research on research reporting practices

• systematic review training and methodological education

• development of structured or automated data extraction systems

The SRED Criteria Manual (Version 1.0) provides a detailed description of the framework architecture, extraction domains, response coding system, priority weighting structure, and calculation of the Extraction Integrity Score. The framework is intended to support systematic review researchers, evidence synthesis teams, and meta-research initiatives seeking to improve methodological transparency and consistency in study data extraction.