A couple of years ago, as this story started to break, I would mention it to scientist friends. "Don't worry," they'd say, "We always have the meta analysis to fall back on."
I don't hear that anymore.
ADD: A critical issue here, which you touch on tangentially, is the mix of motivation and milieu. If I'm making stuff up -- bad data -- other people can identify it and delete it. But if my goal is to appear to be doing good science instead of actually doing it, then it can become extremely problematic both to identify and remedy what I've done.
A lot of "normal" science is done around the margins, with not-so-incredible hypotheses and pedestrian-looking data. Bad science with really poor data fits seamlessly into that model without having any distinguishing characteristics.
https://en.wikipedia.org/wiki/The_Three-Body_Problem#First_C...
If this is true about Chinese clinical data? More generally, if there is any simple tag that covers fraud at this scale? Other people working under that tag will experience deleterious effects for years to come.
The only way I see out of this is a rigorous, self-imposed body of standards. Even then I'm not sure that this will be sufficient.
Think of all those people, all of that money, all of the hundreds of thousands of hours of research. For nothing. Actually, it's worse than nothing because it's been flooding the system with fake data.