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In the realm of conducting systematic literature reviews (SLRs), the question invariably arises: how do these new technologies compare to the trusty spreadsheet? Spreadsheets offer flexibility and a familiar interface, making them the default choice for many researchers. However, as the demands for more streamlined, faster, and AI-assisted SLRs grow, sophisticated tools like Nested Knowledge (NK) are stepping in to revolutionize the process. 


We often get asked about the key differences between Nested Knowledge vs spreadsheet use for tagging and data extraction in the SLR process. While spreadsheets provide a basic framework for organizing data, Nested Knowledge offers advanced features tailored specifically for systematic reviews. From tag hierarchies of visualized concepts to AI-assisted extraction and auto-generated interactive outputs, NK streamlines every step of the review process. To illustrate these tradeoffs clearly, we’ve prepared a comparison table outlining the advantages and drawbacks of each approach throughout the SLR process, including highlighting the functionalities gained by opting for Nested Knowledge over traditional spreadsheet methods. 


Capabilities: Tagging Hierarchies vs. Spreadsheets

TopicTagging & MA ExtractionSpreadsheets
  • Requires understanding of tagging system
  • Includes creating Questions, adding context (descriptions, aliases, Options, Tables)
  • Templates offered
  • Individual column labeling is straightforward
  • Copying projects is straightforward
Data Relationships
  • Relationships built into hierarchy
  • Concepts can be separated, nested, related
  • Data are ‘flat’
  • Column proximity can be proxy for relationships
Automation/ Recommendation
  • AI recommendations provided within the interface
  • No recommendations possible without custom-built software
Data Provenance
  • Full Text annotation possible
  • “Jump-to” highlighted quotations
  • Exact quotation may enable traceability, but
  • No integrated data provenance
Multi-arm Data
  • Requires specialized approaches
  • Either Tag Tables (for flexible data) or MA Extraction (for structured data) needed
  • Must add rows for each arm
  • May lead to awkward/confusing extraction
  • On-the-fly modification offered
  • May require learning & communication
  • May require re-tagging
  • Column addition is straightforward
  • May require communication
  • Requires re-extraction
  • Built into system
  • No straightforward system
  • Version control required
Gathering Process
  • Pre-configured
  • Side-by-side with record
  • Highlighting of relevant concepts
  • Requires configuration
  • Manual extraction
Meta-Analytical Extraction (Quantitative Data Extraction)
  • Enables strict data types if needed
  • Configuration needed, which adds statistical context
  • Automatic Network Meta-Analysis Outputs and Statistics
  • Rapid configuration possible
  • Custom content control possible
  • Flexible entry also possible
  • If properly quality-controlled, meta-analysis by biostatistician
  • Auto-generated Synthesis Outputs
  • Custom spreadsheets or bulk Download possible
  • Dashboards / Live tables possible
  • No auto-generated outputs
  • Custom visuals possible
  • Spreadsheet is its own output

Spreadsheets retain their value as a useful tool for sharing data, which is why they can be readily downloaded post-extraction in the Nested Knowledge software. However, in today’s fast-paced research landscape, clinging solely to spreadsheets for conducting SLRs can hinder progress, efficiency, traceability, and integration of AI tools. Embracing dedicated software not only enhances the performance and outputs reviews but also saves valuable time and resources. Spreadsheets will always have their place, not only for exporting but also for cases where familiarity and flexibility beyond SLR practices are needed, but for everything from structured SLRs with meta-analysis to AI Rapid Reviews, the choice is clear: modern, transparent, specialized tools like Nested Knowledge.

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