searching

To build a systematic literature review search query, you identify key concepts, expand synonyms, and select a query strategy. This process is guided interactively, allowing iterative calibration against result counts before final execution.

The best way to refine search queries using scimesh is through iterative testing. You construct a query, test it against result counts, and use the interactive feedback loops to optimize the search criteria before final execution.

Yes, you can use a predefined protocol. The query building process automatically reads and applies criteria from a defined index.yaml protocol file to ensure the search strategy adheres to your specified review guidelines.

Collaborative query construction handles synonym expansion by guiding users through concept identification. It systematically expands identified terms into synonyms to ensure all relevant literature is captured in the search strategy.

You do not strictly need a protocol file to start, but having an index.yaml allows the system to automatically read and apply predefined criteria. Without it, you manually guide the concept identification and strategy selection.

Interactive query calibration focuses on matching result counts and predefined criteria. It requires manual refinement and feedback loops, meaning the final data retrieval quality depends entirely on the accuracy of your initial concept and synonym inputs.