I presume you mean heteronormative. And it is somewhat heteronormative, but: the paper has a section, "Limitations", that acknowledges this problem and why including more gender options wasn't needed for (and would have made more difficult) the analysis:
> Third, we used self-reported data to identify physician sex, which requires respondents to categorize themselves as either male or female; therefore, we could not capture respondents who were transgender. It is possible that transgender physicians chose to either leave this question blank or select 1 of the 2 available categories, which may lead to a low degree of misclassification. Any misclassification in self-reported sex would likely bias our estimates toward the null.
Although this last sentence is not totally true (if all transgenders labelled themselves as gender A, and they had non-random performance, it could bias the findings in favor/against gender A), it probably is true in the setting of the study, where you can assume transgender doctors label themselves as one or the other sex with a similar proportion, and have either male or female-level performance. Plus there aren't very many.