"The scam in most meta-studies is" that like the studies themselves they are a pack of bull****.

Let us suppose that someone has a substance X that they wish to test for effect Y, say jam and lung cancer. How likely is it that jam causes lung cancer? Rephrase: how likely is it that jam is one of the things that causes lung cancer? Rephrase: what fraction of things cause lung cancer? For the sake of a number, choose 1 in 1000, although the actual likelihood is probably much much less.

Collect data, generate a relative risk, generate a 95% confidence interval on that relative risk, find significance. What inference can you draw?

Choose 1000 things at random and follow the above procedure. Then...

In about 950 cases there will be no link, therefore the parameter (relative risk) will be 1. The interval will contain the parameter, so it will contain 1, so it will not be significant.

In about 50 cases there will be no link, therefore the parameter will be 1. The interval will not contain the parameter, so it will not contain 1, so it will be significant.

In 1 case the will be a link, therefore the parameter will not be 1. The interval might contain the parameter, or not, and it might contain 1, or not.

So if you find significance, the chance of there being a real link is going to be worse than 50 to 1 against. Given that you must go with the most likely option, clearly significance means that you should conclude that you have produced an error, and that there is no link.

In order for significance to mean anything other than error, the probability of there being something to find has to be much better than the significance level of the confidence interval, in this case 1 in 20. (In fact, theoretically, it must be at least 1 in 2.) When scientists do statistics it is never anything like that high. So in all scientific research, significance means error.

If a meta study finds significance, it is because it has found an error.