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Newest first, each with its date. When something changes later, it gets an item of its own, and the earlier ones stay as they were.
Julie now prepares a human check of her screening decisions in any review, on records drawn from those she kept and those she excluded. The report shows how many of the eligible records she kept, and the terms are yours, set before the first sheet goes out.
Four published COPD trials went through data extraction, meta-analysis and GRADE certainty assessment to a manuscript draft. Two AI readers matched 43 and 42 of 48 printed numbers, and neither returned a value that differed from the paper.
Beside the systematic review, Julie now offers pairwise meta-analysis: from an analysis plan you sign to forest plots, GRADE certainty and the manuscript. Every pooling decision stays on the record, under a person’s name.
In a 300-record development sample from a review in progress, Julie’s screening kept 71 of the 72 records that three AI reviewers all judged eligible or possibly eligible, and 77 of the 84 that at least two did. The figures measure agreement with a machine reference, not accuracy.
A source check of four published COPD trials confirmed every numerical entry in an AI-assisted reference set. Six descriptive entries needed clarification.
You choose the standard for each project at the outset, because it shapes what is extracted, how it is rated and what you receive.
If you are running a systematic review, sitting on a guideline panel, or working in an evidence synthesis unit, tell us what you are working on.