BPC-157 actually fixed my bad knee?? Is this placebo — looking for input

Answering the narrow version, because the broad one does not have a single answer. 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.

Dr.NateNeph said:

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

Propensity score matching studies and the trial evidence: when RCTs aren't available for a specific question, propensity score-matched observational studies can provide useful evidence.

A recent PSM study of 12,000 GLP-1 users vs matched controls showed reduced heart failure hospitalization (HR 0.74) over 3 years of follow-up[1].

These results complement the RCT data and suggest the benefits translate to real-world populations.

Dr.BariatricHTX 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…

Same position here, arrived at the long way round. 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.