The concept of scaled quadratic variation was originally introduced by E. Gladyshev in 1961 in the context of Gaussian processes, where it was defined as the limit of the covariance of the underlying Gaussian process. In this paper, we extend this notion beyond the Gaussian framework for any real-valued continuous function by formulating it in a pathwise manner along a given sequence of partitions. We demonstrate that, for classical Gaussian processes such as fractional Brownian motion, this pathwise definition coincides with the traditional one up to a constant factor. Furthermore, we establish that the scaled quadratic variation is invariant under smooth transformations and satisfies a pathwise Itô isometry-type result, derived without relying on any expectation arguments.