Dr.PainCLE said:

Read four things before the headline number.

This is where I part company with the consensus forming above. I would add the less popular caveat: these trial populations under-represented several groups, older adults and the highest BMI categories among them. The results probably generalise, and "probably" should be stated as an assumption rather than dropped.

LarryQC_SD 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…

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 25,000 GLP-1 users vs matched controls showed reduced all-cause mortality (HR 0.81) over 5 years of follow-up[1].

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

amsterdam_pete said:

I would add the less popular caveat: these trial populations under-represented several groups, older adults and the highest BMI categories among them.

Forest plot interpretation for the the trial evidence meta-analysis: when reading the pooled estimate, pay attention to:

1. Point estimate (HR/RR/OR) — center of the diamond
2. Confidence interval width — precision of the estimate
3. I² statistic — heterogeneity across studies
4. Individual study weights — are results driven by one large trial?
5. Prediction interval — range of plausible true effects in future settings

The the trial evidence meta-analysis shows a pooled RR of 0.79 (95% CI 0.71-0.84), I²=52%. This is a robust and consistent effect.

One thing that is still open after Dr.EM_Chicago’s answer:

What did you change at the same time, and can you separate the two now?

Reporting back.

Follow-up: I read the paper rather than the summary and the qualifier I was missing was in the second paragraph of the results.