This one has a reasonably settled answer, so here it is. The gap between trial results and real-world results is consistent and it is not fraud. Trial participants get titration by protocol, scheduled contact, free drug and dietetic support; removing that infrastructure costs a few percentage points every time it has been measured. When your own curve sits below the published mean, that is the likeliest explanation before anything about you or your material.

I would rather be corrected than agreed with, if it comes to it.

Dr.KarenChen said:

The gap between trial results and real-world results is consistent and it is not fraud.

Dr.KarenChen said:

...regarding the trial evidence...

I think this is an underappreciated point. To expand on it with some data:

A recent meta-analysis of 18 RCTs (n=15,600) found that the trial evidence was associated with a robust effect size across diverse patient populations[1].

The NNT was 15, which is comparable to statins for secondary prevention. That's a strong clinical argument for this approach.

Dr.BariatricHTX said:

I keep finding that the number in the press summary and the number in the paper are not the same number, and the difference is always in the same…

Can confirm the pattern Dr.BariatricHTX describes. Read four things before the headline number. The population, because trial populations are selected and supported in ways that real cohorts are not. The comparator, because "better than placebo" and "better than the current standard" are different claims and get reported identically. The primary endpoint as pre-registered, because a secondary endpoint promoted after the fact is a hypothesis rather than a finding. And the completion rate, because a large effect in the half of participants who finished is a different result from a large effect in everybody enrolled.

Adding the clinical framing, because it changes how the question reads.

Bayesian meta-analysis perspective on the trial evidence: traditional frequentist meta-analyses report point estimates and confidence intervals. Bayesian approaches provide probability distributions that are more intuitive for clinical decision-making.

For example: "There is a 98.5% probability that semaglutide 2.4mg produces >10% weight loss vs placebo" is more actionable than "RR 3.4, 95% CI 2.8-4.1, p<0.001."

The the trial evidence evidence is strong under both frameworks, but Bayesian analysis better communicates the degree of certainty for individual patient counseling.