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
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The Screening Model uses AI to learn from screening decisions within a specific nest, generating inclusion probabilities based on configuration.
You may use the screening model in two ways:
Both methods require the model to be trained but the first only displays probabilities, allowing you to order studies in the screening queue by likelihood of inclusion or bulk screen studies by inclusion probability. Whereas Robot Screener actively takes the place of a second reviewer, and hides the probabilities for the remaining reviewers by default.
The below guidance is specifically for using Robot Screener, for information on training the model for probability generation only and general information on how the model works see Using and Interpreting the Screening Model.
The Screening Model can be used to power AI-assisted screening, replacing one expert in Dual Screening processes.
Robot Screener may only be turned on in Dual Screening modes and it's important to note at what stage they are generated and the language used:
To learn about configuration settings, which enable you to toggle Manual updating vs. Automatic and Displayed vs. Hidden, see the Settings page.
In its default setting, the Screening Model must be run manually. To do so, click “Train Screening Model” on the Screening panel:
Once the modal opens, click “Train New Model.”
To provide the model with sufficient information to begin understanding your review, we require 50 total adjudicated screening decisions with 10 advancements or inclusions before the model can be trained. If there is insufficient evidence to train the model, complete more adjudicated screening (2 reviewers and 1 adjudicator) until the “Train New Model” button becomes available.
It may take a minute to train, after which it will populate a histogram on the left. From then on, each record will show a probability of inclusion or advancement:
