Basics of Systematic Reviews

At this stage of your systematic review, you will examine the full text of included studies and systematically collect key information in a structured table format. This process helps summarize the studies and makes it easier to compare findings.

Key Steps for Data Extraction:

1. Ensure you have access to the full text of all included studies.
2. Identify the specific information you need to extract from each study.
3. Choose a method for collecting the data.
4. Create a data extraction table.
5. Test the data collection table (optional step).
6. Extract and record the data.
7. Review your collected data for accuracy and completeness.

For reliability, it’s recommended that at least two people independently extract data from each study. This process can be done manually or with specialized software.

Data extraction involves creating structured tables to summarize your findings. You might use an evidence table (which includes detailed study information, with health-related disciplines sometimes using the PICO framework) and/or a summary table (which provides a high-level overview of study results). These tables help determine whether studies qualify for quantitative synthesis.

Recommended Data Extraction Tools

Cochrane RevMan

• Provides standardized collection forms for study characteristics, interventions, outcomes, and quality assessments.
• Data must be entered manually, and form elements are fixed.
• Available as a free software download.

DistillerSR

• Web-based software designed for screening and data extraction.
• AI-enhanced data extraction with templates and configurable forms.
• Pricing is based on a subscription model

Data Extraction Tools from your Chosen Screening Software

• Both Covidence and Rayyan offer data extraction tools on a subscription basis.

Spreadsheets & Databases (Excel, Google Sheets, Microsoft Access)

• Spreadsheets allow dropdown menus and range checks to reduce data entry errors.
• Databases can structure information into categories like citation details, participant demographics, intervention details, and outcomes.

Your data collection should align with your systematic review question. Reviewing similar systematic reviews can help guide your choices.

Common Data Points for Intervention Studies

• Study/Article Information: Author(s), publication year, title, DOI
• Study Characteristics: Study type, participant recruitment method, allocation process, quality assessment, level of evidence
• Participant Demographics: Age, sex, ethnicity, health conditions, other relevant factors
• Intervention Details: Dosage, delivery method, frequency, duration, study setting, quantity.
• Outcomes Measured: Both quantitative and qualitative results

If your review includes the statistical synthesis of data, additional details like sample sizes, effect sizes, reliability measures, dependent variables, and statistical tests may be required.  For qualitative studies, data extraction may focus on aspects such as theoretical frameworks, data collection methods, and researcher bias.