Hello Fabio,

There isn't a way to automatically apply your inclusion or exclusion criteria based on your search strategies but there are a number of ways to optimise the process.

You can bulk apply a code based on a particular parameter. For example, if one of your exclusion criterion was something like  'Exclude items pre 2000' you could order your items by year (or search on a year) and select the items that meet or don't meet this criteria. You can then apply that code to the items in one operation by right clicking the code and selecting 'Assign selected items to this code'.

Something that might be closer to what you have in mind is to use text-mining to determine which items are more likely to be an include. There are text-mining functions available in EPPI-Reviewer that you can use. You will find them described in the User manual under 'Text mining' (pg 89). The basic process is that you start out by manually screening a number of items. You then use the text mining engine to examine your included items and to order your remaining items from 'most likely to be an include' to 'least likely to be an include'. You would then screen through your remaining items knowing that the items you are looking in the early stages were more likely to be an include. 

A third mechanism that we are introducing is 'Priority screening'. This mechanism uses text mining to prioritise the items that the screener sees based on the coding that has already taken place. The system actively learns from your previous work and continues to make adjustments based on the work you are doing. This function is particularly useful for very large reviews (many 10's of thousands of items) where you may not have the time or resources to screen all of the items before moving onto the next stages of the review. At the present time we need to manually activate this function as it requires a bit of intervention on our part. If you think this might be appropriate for your review please let us know and we can see about making it available to you.

Best regards,

Jeff