# Ordered probit model prediction: why highest probabilities and not number of thresholds exceeded?

I'm running ordered probit regression models with polr in R. This question states that for prediction, polr returns the category with the highest probability for given $x$. I would have expected that the predicted category is equal to the number of estimated thresholds ($\hat{\zeta}$) exceeded by $x' \hat{\beta}$ (what imo would be in line with the model assumptions). Is there a reason, why the polr-approach is "better" then the thresholds-approach?