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wiki:autolit:extraction:dual

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Dual Extraction

Dual Extraction is a quality-controlled extraction process, where two users independently extract data from each article, and then all data are adjudicated by an Administrator.

The Admin adjudicates any disagreement between Reviewer A and Reviewer B and sets the final determination for each study. For example, if Reviewer A extracts the mean age as 70 but Reviewer B extracts it as 71, the Adjudicator will then need to choose between those values and identify the correct one.

Only those with Admin privileges can serve as Adjudicators, but any user can serve as a Reviewer

This feature is useful to ensure that your team curates the most accurate and high qualitly data possible. Dual extraction can help with this since it has been shown that dual extraction results in fewer errors than single extraction. (source)

Configure Dual Extraction

To configure dual extraction, go to the Admin page under settings and then scroll to the Extraction section. Then, toggle to dual extraction to turn this feature on.

Note: Toggling back from Dual Extraction to Standard Extraction will ONLY save final adjudications, and all data associated with individual users' extractions will be lost!

Dual Extraction Steps

1. Turn on dual extraction using the steps shown above.

2. Two users must independently extract the data.

If you need help with this, check out this page. If two users don't independently extract the data in a given study, the study will not appear in the Adjudicate Extraction tab.

3. Adjudicate the data

Places where the Reviewers disagreed will be highlighted in red. There are three columns: Reviewer A, Reviewer B, and Final. The adjudicator will put the correct data in the Final column, adjudicating differences between the reviewers. The adjudicator can choose to input the same data as either of the reviewers or neither of them if they were both wrong. To enter in data, hit the plus sign and fill in the cells.

wiki/autolit/extraction/dual.1656943917.txt.gz · Last modified: 2022/07/04 14:11 by nicole_hardy_alumni.brown.edu