Love it or hate it, machine learning and artificial intelligence hacked their way into our lives, and those who can use them to their advantage live longer and work shorter.
If you are one of those systematic reviewers still using EndNote for screening your search results. In that case, I must respectfully guide you towards the industry news: Priority Screening is here to stay.
We cannot waste more hours on screening; we are done going through all that sensitive search results; the machine is here to help without interrupting your independence.
What is Priority Screening?
In the systematic review and evidence synthesis context, priority screening is screening those search results that are identified by the machine learning model as relevant/undecidable as the priority. The rest of the results could be either ignored or viewed and marked as irrelevant by one human reviewer.
Priority Screening has three requirements:
- Human Screener: human reviewer starts the routine screening process by including/excluding the records.
- Machine Learning Model: after a certain number of eligibility decisions by humans, a machine learning (ML) model can be built (automatically by the screening system or human command) and rank the most relevant records on top of the list of records for screening. As the screening goes on, this ML model can be updated (automatically by the screening system or human command).
- Progress Graph: The screening continues until the time that the screening of new records by the human screener does not lead to the inclusion of any relevant record. For example, this can be set as part of the review protocol: “If during priority screening the reviewer excludes 100 continuous records, the screening will stop”. Since counting the numbers during the screening might not be pragmatic, a Progress Graph could be a good indicator. If you are interested in knowing more about when to stop, please read…
