Citation metrics are analytic measures used to evaluate the usage, impact and dissemination of scientific research. Traditionally, citation metrics have been independently measured at each level of the publication pyramid, namely at the article-level, at the author-level, and at the journal-level. The most commonly used metrics have been focused on journal-level measurements, such as the Impact Factor and the Eigenfactor, as well as on researcher-level metrics like the Hirsch index (h-index) and i10 index. On the other hand, reliable article-level metrics are less widespread, and are often reserved to non-standardized and non-scientific characteristics of individual articles, such as views, citations, downloads, and mentions in social and news media. These characteristics are known as 'altmetrics'. However, when the number of views and citations are similar between two articles, no discriminating measure currently exists with which to assess and compare each articles' individual impact. Given the modern, exponentially growing scientific literature, scientists and readers of Science need optimized, reliable, objective methods for managing, measuring and comparing research outputs and individual publications. To this end, I hereby describe and propose a new standardized article-level metric henceforth known as the 'Individual Impact Index Statistic', or $i^3$ for short. The $i^3$ is a weighted algorithm that takes advantage of the peer-review process, and considers a number of characteristics of individual scientific publications in order to yield a standardized and readily comparable measure of impact and dissemination. The strengths, limitations, and potential uses of this novel metric are also discussed.