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Evidence-based dental practice requires the developmment and evaluation of protocols that en-sure translational effectiveness: that is, the efficient incorporation of the best available efficacy and effec-tiveness findings in specific clinical dentistry settings and environments. Evidence-based dentistry predi-cates the synthesis of research for obtaining the best available evidence in a validated, stringent, systematic and unbiased fashion. Research synthesis is now established as a science in its own right, precisely because it adheres to the scientific process that is driven by a research question and a hypothesis, follows through clearly defined methodology and design, yielding quantifiable data that are analyzed statistically, and from which stringent statistical inferences are drawn. The conclusions from the protocol of research synthesis define the best available evidence, which is used in the process of evidence-based revision of clinical practice guidelines. One important hurdle of the process of applying research synthesis in evidence-based dentistry lies in the fact that the statistical inferences produced by research must be translated into clinical relevance. Here, we present a model to circumvent this limitation by means of text analysis/mining protocols, which could lead the path toward a novel, valid and reliable ap-proach for the inferential analysis of clinical relevance.
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