This only matters in calculations where randomness is important, i.e. Monte Carlo method.
Nevertheless, if you don't use the least significant bits, and if the constants are carefully chosen, MLCG passes most of the hardest statistical tests. For example it passes all DIEHARD tests, and most of TESTU01.
In addition, the described algorithm "is on-line - that is, it requires no preprocessing and can generate each element of the sample in constant expected time." (Quoting from the original paper.) It needs only an upper bound on the number items, while your suggestion requires knowing the number beforehand.
You also need an MLCG over the range 0..2^64-1 to match the given algorithm. I believe that requires some extra code to handle potential overflow in a MLCG, so adds a few more lines to your estimate.
