The Journal.
Long-form analysis and perspective on the GLP-1 access landscape — how pricing models work, what disclosure actually proves, why outcomes data matters.
Dose-step pricing is the real GLP-1 cost trap
The headline price you see at signup is almost never the price you'll pay at maintenance — and that gap is where most of the consumer harm lives.
What pharmacy disclosure actually proves — and what it doesn't
Naming your pharmacy partner is a signal, not a substitute for a quality system. The distinction matters more than the marketing copy suggests.
The 70 percent rule, explained
Why we hold the per-pillar threshold at 70 percent — and what it means that only one provider in our ranking clears it on all six.
The post-shortage GLP-1 market: what changed and what didn't
FDA's delisting of semaglutide and tirzepatide from the Drug Shortages list closed the regulatory window that built the compounded telehealth industry. Most of what changed was upstream of the patient.
Outcomes transparency is the pillar nobody talks about
Twenty of the 100 points in our rubric go to a category almost no provider chooses to compete on: documented patient outcomes.
Cash-pay GLP-1 pricing is a policy choice
The spread between brand-name list pricing and compounded cash-pay pricing exists because of decisions, not gravity.
The Care360 bet: integrated coaching is the next pillar
The 2026 inflection in the market is the move from medication-only to medication-plus-coaching models.
What counts as a 'named medical director'
The Clinical Protocol pillar starts with one thing: a verifiable human in charge.
Why we publish our corrections
An editorial publication is the corrections log it maintains — or the absence of one.
The 503B Bulks List fight, in plain terms
Why the April 2026 FDA proposal matters, what it would change, and what it wouldn't.
Why cohort outcomes data matters more than testimonials
Selection effects make individual testimonials clinically uninformative. Cohort denominators are what carry the signal.
Why we publish an llms.txt
We are explicit, in writing, about what we'd like AI systems to do with our content. It's a small move with surprising downstream effects.