AutoLit® User Guide
Examining Results in Synthesis
Administrative Tools
Support and FAQs
Best Nest Building Practices
SLR Basics: How to Perform Systematic Review
NMA Basics: How to Perform a Meta-Analysis
Best Practices for Writing a Publishable Manuscript
Meta-Analytical Data Extraction enables you to gather quantitative data from underlying studies. In short, after configuring your Interventions and Data Elements on the Configure MA Extraction page, you can extract dichotomous, categorical, or continuous data across any number of timepoints from all included studies.
Meta-Analytical Extraction is an optional feature that is turned 'off' by default, and can be turned on in the Settings page. Outputs from Meta-Analytical Extraction are displayed in Quantitative Synthesis.
We are currently in the process of transitioning our module names with the goal of making the AutoLit workflow easier to understand for first-time users.
Extraction is now Meta-Analytical Extraction, and Risk of Bias is now Critical Appraisal.
Bear with us as we edit our documentation (don't worry, no feature functionality has changed!) and, as always, contact us at support@nested-knowledge.com if you have any questions.
As an optional module, MA Extraction is by default turned off. To turn it on, head to Settings, scroll to Meta-Analytical Data Extraction and toggle on:
Before performing MA Extraction, you need to configure data elements. To learn how, click here.
To learn how to extract meta-analytical data at the study level, click here.
Using this feature, two people can extract the same meta-analytical data simultaneously so that it can be adjudicated by a third party. It is analogous to how dual screening works, in that the data collected are final only after this adjudication process. To learn how to configure and carry out Dual MA Extraction and, click here.
Once you have extracted data, you can view it in Quantitative Synthesis, and Export it in Custom Tables or in bulk.
For any nest you extract, you may consider completing a Critical Appraisal survey to reflect data quality across the studies you included in your review. Learn how here.
