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:

• Generate inclusion probabilities to be displayed on records and assist in your own manual screening.
• Dual modes only: Turn on Robot Screener to replace an expert reviewer, which makes decisions based on these probabilities.

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:

• Dual Standard Mode: Robot Screener replaces a reviewer in the singular round of Screening based on Inclusion Probabilities.
• Dual Two Pass Mode: Robot Screener replaces a reviewer in the Title/Abstract round of Screening only, based on Advancement Probabilities.

To turn on Robot Screener, head to Nest Settings –> Screening Model, toggle on Robot Screener.

Not displayed? You must be in a Dual Screening mode to use Robot Screener.

Want to use Automatic Training for use in manual screening instead? Learn more here.

When toggling Robot Screener, you'll be presented with an instructional modal:

Highlighted in red are the requirements for training the screening model, and the actual numbers based on the progress made in your nest. You will not be able to turn on Robot Screener until these minimum requirements are met.

Once trained and turned on, the Robot is assigning both inclusion probabilities and actual screening decisions to the remainder of records in the queue. Currently, Robot Screener does not assign exclusion reasons, so decisions are displayed as “Advance”/“Include” or “Robot Excluded”. The records that Robot Screener makes a decision on will still need an additional human to screen these records as a second reviewer, and a human adjudicator to make the final decision on these records. This means that each record will always have two pairs of eyes to review.

At any time, you may wish to view how the screening model is performing. To view the model performance, navigate to Nested Settings –> Screening model –> View Screening model.

This will display a histogram under the “Predictions” tab, a table of various Cross Validation statistics, and an explanation as to how to interpret these values. You can also view the Robot Screener recommendations in the Screening model modal. Select “Advance”/“Include” to view studies the Robot has advanced/included, or “Exclude” for excluded studies– these are both shortcuts that take you to Study Inspector to show you the corresponding subsets of studies. Otherwise, the filter can be manually added from Study Inspector. From this modal, you can also delete the model if you wish to start again from scratch.

Learn more about interpreting the model, its performance and how it works here.

The best way to improve Robot Screener, is to adjudicate records, since these are the decisions it trains on. We recommend, if you can, have your adjudicator make their final decisions on the Adjudicate Screening page after every 50 studies are screened for best model performance. For reference, the following is what adjudicators will see for records that have one human and one Robot Screener decision applied:

Robot Screener has been validated in several published studies assessing its decisions in comparison to human decisions across multiple reviews and review types.

• Internal validation: Nested Knowledge assessed the Recall and Precision of Robot Screener in 19 projects with over 100,000 cumulative decisions, finding significantly lower Precision than humans (that is, humans correctly exclude studies more often) but significantly higher Recall– meaning that the Robot Screener misses fewer includable records. In this analysis, Robot Screener was found to have 97.1% Recall.
• External validation: Cichewicz et al. assessed diagnostic accuracy across many metrics in 15 projects, finding Robot Screener had significantly lower Precision than humans and no statistical differences in Recall between Robot Screener and humans. Robot Screener had fewer overall False Negatives, but no significant differences were found.
• Estimates of time savings using different modes of Robot Screener have been previously published online.

You can see a deeper summary of the Validation Studies and their implications here.