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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

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Data extraction and comparison for complex systematic reviews: a step-by-step guideline and an implementation example

Mohamed Afifi^1,2, Henrik Stryhn³, Javier Sanchez³

¹Department of Animal Wealth Development, Biostatistics Section, Faculty of Veterinary Medicine, Zagazig University, Zagazig, Ash Sharqia Governorate, 44519, Egypt. MAAfifi@vet.zu.edu.eg.

Systematic Reviews

|December 2, 2023

Summary

This summary is machine-generated.

This guideline offers a 10-step process for designing data extraction (DE) tools and comparing extracted data in complex systematic reviews (SRs). It aids SR teams in managing large datasets and resolving reviewer discrepancies efficiently.

Keywords:

Complex Data extraction Database Epi Info Guideline R Systematic review

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Published on: September 20, 2018

Area of Science:

Medical Informatics
Evidence Synthesis
Research Methodology

Background:

Data extraction (DE) in complex systematic reviews (SRs) presents significant challenges due to multiple interventions, outcomes, and research questions.
Existing DE guidance often focuses on meta-analysis, lacking specific strategies for complex SRs and efficient discrepancy resolution.
Comparing large, independently extracted datasets is cumbersome, hindering the systematic review process.

Approach:

A 10-step guideline is presented, structured into planning, database building, and data manipulation phases.
The guideline emphasizes determining data structure and provides practical steps for designing and building DE tools.
It illustrates the application of Epi Info and R for database construction and data comparison, including discrepancy resolution.

Key Points:

The guideline is software-agnostic, focusing on general principles applicable to various complex SR projects.
Epi Info's features for relational databases and data validation are highlighted for building DE tools.
Specific R libraries are recommended to facilitate data comparison and resolve discrepancies between reviewers.

Conclusions:

Adopting this guideline can help systematic review teams develop effective DE tools tailored to complex projects.
Epi Info serves as a viable alternative to commercial DE software for complex systematic reviews.
The proposed methodology aims to streamline data extraction and enhance the reliability of systematic reviews.