  We revisit the Stochastic Score Classification (SSC) problem introduced by
Gkenosis et al. (ESA 2018): We are given $n$ tests. Each test $j$ can be
conducted at cost $c_j$, and it succeeds independently with probability $p_j$.
Further, a partition of the (integer) interval $\{0,\dots,n\}$ into $B$ smaller
intervals is known. The goal is to conduct tests so as to determine that
interval from the partition in which the number of successful tests lies while
minimizing the expected cost. Ghuge et al. (IPCO 2022) recently showed that a
polynomial-time constant-factor approximation algorithm exists.
  We show that interweaving the two strategies that order tests increasingly by
their $c_j/p_j$ and $c_j/(1-p_j)$ ratios, respectively, -- as already proposed
by Gkensosis et al. for a special case -- yields a small approximation ratio.
We also show that the approximation ratio can be slightly decreased from $6$ to
$3+2\sqrt{2}\approx 5.828$ by adding in a third strategy that simply orders
tests increasingly by their costs. The similar analyses for both algorithms are
nontrivial but arguably clean. Finally, we complement the implied upper bound
of $3+2\sqrt{2}$ on the adaptivity gap with a lower bound of $3/2$. Since the
lower-bound instance is a so-called unit-cost $k$-of-$n$ instance, we settle
the adaptivity gap in this case.
