The keywords and additional questions are used to guide screeners as they are reviewing articles; they do not affect Active Screener’s machine learning capability. Only the content of the articles themselves (title, abstract, etc.) is used by the machine learning. The machine learning is based on whether screeners include or exclude a specific reference. Active Screener automatically prioritizes articles as they are reviewed and suggests the next articles to screen based on previously included articles. Initially, articles are going to be presented in random order because you haven’t provided any sort of feedback to help the system build a model of relevance. But as you start screening, the system will start looking at the articles you include and exclude and start changing the presentation order of references, in order to show you more references that are likely to be relevant.
Keywords are used to draw your eye to important words in titles and abstracts as you are screening. If you add the word “zebrafish,” and add it as an included keyword, whenever this term appears in the title or abstract it will be highlighted in the inclusion color. On the other hand, if you’re not interested in “mouse,” you can add that to the exclusion keywords and if this term appears in an article it will be highlighted it in the exclusion color.