Is this actually an example of selection bias? In Lesson 3, Chapter 3 of Miguel Hernán's edX course on causal diagrams, he presents this DAG:

It represents a study on the effect of hormone therapy on lung cancer (whether hormone therapy causes lung cancer). Among women with lung cancer in Boston, a random sample of 1,000 has been selected for the study. Among women without lung cancer in Boston, a random sample of 1,000 has also been selected for the study. This accounts for the arrow between lung cancer and selection (i.e. outcome-based selection).
Then he adds that only women with hip fractures were surveyed ("they can't run away from you"), which accounts for the arrow between hip fracture and selection.
Finally, he says that hormone therapy actually reduces the risk of hip fracture, so the final arrow between hormone therapy and hip fracture is added, creating an open backdoor path.
My question is, if only women with hip fractures were surveyed, doesn't that mean we are conditioning on hip fractures? If so, there should be a square around hip fracture, the backdoor path is actually blocked, and there is no selection bias.
 A: If it's true that only women with a hip fracture were selected, then there is no association between hip fracture and anything in the selected population. This would amount to saying something like "among women with hip fractures, there is an association between having a hip fracture and having lung cancer." Clearly, this doesn't make sense. An association requires variation in two variables. You can't talk about an association between a variable and a conditioned-upon variable.
If what he meant was "women with hip fractures were more likely to be selected," then one is not conditioning on hip fracture but rather on selection, which is caused by fracture, as the DAG displays. He may have misspoken in the video, because this is clearly what he intended to mean.
A: 
My question is, if only women with hip fractures were surveyed,
  doesn't that mean we are conditioning on hip fractures? If so, there
  should be a square around hip fracture, the backdoor path is actually
  blocked, and there is no selection bias.

Notice that even if the DAG were only $A \rightarrow Y \rightarrow C$ the post-interventional distribution $P(Y|do(A))$ is not non-parametrically identified, since $P(Y|do(A)) = P(Y|A) \neq P(Y|A, C)$ and you only observe $P(Y|A, C =1)$. Thus, if your target estimate is $P(Y|do(A))$, there is selection bias even without the path $A \rightarrow F \rightarrow C$. 
For selection bias, you might want to check Bareinboim and Pearl and more recently Correa, Tian and Bareinboim.
