The article presents it as if it was some big revelation in 2005, but I'm not sure it was as big as implied. I mean some of the specifics were, but I trained in my residency between 2000-2003, and we were very much trained to be skeptical of studies, and to question results. Evidence-based medicine was in strong force.
His model predicted, in different fields of medical research, rates of wrongness roughly corresponding to the observed rates at which findings were later convincingly refuted: 80 percent of non-randomized studies (by far the most common type) turn out to be wrong, as do 25 percent of supposedly gold-standard randomized trials, and as much as 10 percent of the platinum-standard large randomized trials.
Non-randomized studies are the most common, of course. They are the cheapest to perform by far. but whenever I read non-randomized study, I think "interesting" but realize it doesn't mean anything. Correlation does not equal causation. These non-randomized studies though are the ones that raise questions and possibilities that later fund/justify more costly randomized-controlled studies.
And something to be aware, which is perhaps part of the purpose of this article, is that there is a reporting/publishing bias. Negative and neutral results simply don't get reported in journals. You form a hypothesis, perform a study, and get negative results -- well, you're not going to try to publish it. Statistically speaking, there's going to be some bell curve distribution about the actual result. So let's say the actual result is "0" (no effect) for a drug or treatment. If you do enough studies, you'll get a few that fall on the positive side, which I presume is what accounts for some of the false positives.