Let $(\mathcal F_t)$ be an arbitrary filtration on $[0,T]$ and $(X_t)$ a sample continuous stochastic process on $[0,T]$ that is a martingale with respect to $(\mathcal F_t)$. The quadratic variation of $(X_t)$ is given by a deterministic function $[X]_t=g(t) \neq 0$. We suppose that $g$ is smooth almost everywhere on $(0,T)$.
I would like to use $(X_t)$ to construct a Brownian motion adapted to $(\mathcal F_t)$ using the Levy characterisation theorem. The latter says that if a process $(Y_t)$ is an $(\mathcal F_t)$-martingale with $[Y]_t=t$, then $(Y_t)$ is a Brownian motion adapted to $(\mathcal F_t)$.
So if I take $$Y_t= \int_0^t \frac{1}{\sqrt{g'(s)}} dX_s,$$
Then $[Y]_t= \int_0^t \frac{1}{g'(s)} d[X]_s = \int_0^t \frac{g'(s)}{g'(s)} ds =t$. But is $(Y_t)$ an $(\mathcal F_t)$-martingale ?