Taking the question as asked, rather than the general version of it. 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.

LibrarianMeg said:

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

LibrarianMeg said:

...regarding the trial evidence...

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

A recent meta-analysis of 12 RCTs (n=8,400) found that the trial evidence was associated with a significant effect size across diverse patient populations[1].

The NNT was 8, which is comparable to metformin for T2DM prevention. That's a strong clinical argument for this approach.

labquiet_amy said:

My own curve sits about four points below the published mean and I spent two months assuming that meant something was wrong with me or with my…

This matches mine closely enough to be worth saying so. 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.

From the other side of the consultation, briefly.

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.