CagriSema ctgov enrollments: 609, 300, 178, 3400, 100 — what am I missing?

Posting lag is real. Completed on ctgov often sits without results for a year+ unless it’s a registrational readout. The 300 and 178 may be smaller and still unpublished.

Same. The 3400 record nags me — why isn’t it the headline?

Because 3400 might be a different molecule or Phase 3 under another registry umbrella. Read arms, not just N.

The recruiting NCT07411560 is the one I’d expect people to parse first. Weekly GIP plus amylin is a combo design, not CagriSema exactly.

That combo design makes me wonder: cagrilintide or another amylin analog?

I did the same spreadsheet thing with my own scale for 14 weeks and the "enrollment" numbers that mattered were my waist (down 3.5 inches) and sleep (up 45 min). The enrollment numbers you listed look like different study phases, not one pool.

Weirdly, my weight stalled twice for 11 days both times right before a whoosh. Are you logging dates or just counts? That timing gap might explain why none line up.

100 and 3400 in the same class of trial still feels like apples to oranges.

I gave up on ctgov math and started tracking my walking pad minutes instead. Way less confusing.

Honestly the 3400 one being active-not-recruiting is the tell — that's a big outcomes trial, not a cute little PK study.

I stopped trying to make trial numbers mean anything for my own timeline after I spent a weekend convinced my 2 lb bump was fat, then it vanished by Tuesday. Now I only weigh Friday mornings after a normal Thursday. Are you comparing start dates or just raw N? Dates usually untangle this faster than counts.

check enrollmentType. Half those records say “Anticipated,” not actual randomized. Completed ones can still show the old estimate. I got burned by that tracking a different trial. Also filter by phase and masking. That 100 is probably a sub-study or PK arm, not a mini efficacy trial. Are all five Phase 3? If not, the spread makes more sense. Night shift brain says sort by start date, not the enrollment number.

I’d filter by condition and primary endpoint before the raw N. The 178 could be a T2D or OSA cohort while the 3400 is an obesity outcomes trial — different populations, different signal-to-noise.

My daily weigh-in taught me the same: compare today to your own 7-day trend, not someone else’s number. What are the five conditions listed? That’d explain more tan enrollment alone.

I’ve got the same five rows in my sheet. The thing that broke my brain: the 300 record’s last update is 2023, while the 609 was updated this spring. So the Ns are snapshots from different years, not a clean comparison.

Also, if you pull the API JSON instead of copying the web page, one of those records shows a different enrollment count because a site amendment lagged. Are you tracking “Last Update Posted” too? That column is the only reason I haven’t deleted the tab.

i’d add a “number of sites” and “countries” column — that’s what finally made my sheet make sense. A 3,400-person trial spread across 200 sites reads very different from a 100-person single-country record, and some tiny Ns are likely PK/food-effect or Japan/China bridging, not efficacy.

Also check “Last Update Posted” versus “First Posted”; one of my rows changed N without changing NCT. Are you logging primary completion dates too? That’s the column that actually predicts when we’ll see topline.