I am not very convinced by Ioannidis’s counterpoint in that thread. It felt more like arguing for the sake of arguing. You can argue about the validity of anything in your model, whether frequentist or Bayesian, after your trial is completed. Example: proportional hazards in immunotherapy trials in oncology. A Bayesian approach can be advantageous in that you can prespecify parameters for aspects of your model that you are not sure whether you will need them or not. In the proportional hazards example one can use a prior for the proportional hazards assumption (time x treatment interaction effect).
I would gauge validity based on whether your inferences work or not in real life. For example, this trial (priors + model + data generated) gave us useful dose-finding information that has been externally validated in the sense that the doses we came up with in that trial are being efficiently used in subsequent phase 2 trials. There is always room for improvement of course and an updated design, using the same data structure and motivating example, is currently under peer review in a statistical journal.