Editorial reviews. Affiliate fees from some providers don't affect rankings. Disclosure

Editorial Scaffolding

How HormoneScore works

The exact formula behind our brand scoring. 6 weighted dimensions. Published methodology. No paid placement.

Our editorial trust signals

8.5/10
  • Transparency9.0/10

    Methodology + weights published publicly.

  • Methodology rigor8.5/10

    Composite of 6 weighted sub-dimensions with published thresholds.

  • Sources cited8.0/10

    Outcome stats require verifiable source URLs.

  • Clinical reviewers9.0/10

    NAMS-certified team on review every brand.

  • Conflict disclosure8.0/10

    Affiliate disclosure on every page. Scoring unaffected by revenue.

Every brand reviewed here gets a 0-100 HormoneScore composite. It's the only metric we publish that influences ranking. Errors update within 7 days — see corrections log.

100pointsClinical rigor (25%)Editorial transparency (20%)Pricing transparency (15%)Patient experience (15%)Medication quality (15%)Access & continuity (10%)

Six weighted dimensions

Total = 100 points. No category exceeds 25% to prevent single-dimension dominance.

  • Clinical rigor25 pts

    Provider credentials (board cert, NAMS), prescribing model (sync vs async), labs required, follow-up cadence

  • Editorial transparency20 pts

    Disclosure compliance (FTC), conflict-of-interest declarations, source citation density, correction history

  • Pricing transparency15 pts

    Itemized cost breakdown, insurance acceptance, no-hidden-fees policy, refund/cancellation clarity

  • Patient experience15 pts

    Trustpilot/BBB ratings (filtered for fake reviews), Reddit sentiment, Sitejabber, response time data

  • Medication quality15 pts

    Pharmacy partnerships (503A vs 503B for compounded), FDA-approved vs custom-formulated, lot transparency

  • Access & continuity10 pts

    States served, provider continuity (same vs rotating), evening/weekend coverage, in-person referral pathway

Automatic disqualifiers

Any of these = brand cannot appear in ranked lists, regardless of score:

  • Pending FDA Warning Letter (active)
  • DOJ/FTC settlement in past 24 months involving consumer harm
  • State medical board action against ≥3 affiliated providers
  • Pattern of fake-review allegations with documented evidence
  • Refusal to disclose ownership structure
  • No licensed clinician on record (clinic-only model)

What we test ourselves

  • Signup flow — every brand reviewed gets a manual signup test by a real human (not bot/scrape)
  • Time-to-prescription measured from account creation to first script availability
  • Provider message response time over 7-day window
  • Refund process tested on subset (we cancel within 14d to test friction)
  • Pricing screenshots archived monthly — see "Last priced" stamp on each brand page

Update cadence

  • Pricing — refreshed monthly, more often if brand changes pricing midcycle
  • Scores — recomputed quarterly
  • Time-sensitive content (FDA guidance, drug shortages) — within 72 hours of change
  • Corrections — 7-day SLA from report to public update

Conflicts of interest

We accept affiliate commissions from some (not all) brands listed. Commission rates do not affect score or ranking — this is enforced by structural separation: the editor scoring a brand cannot see commission rate data. We also publish non-affiliate alternatives (e.g., Cost Plus Drugs) when they're materially cheaper. See full disclosure on affiliate disclosure.

What would change our mind

  • New peer-reviewed evidence overturning a clinical claim we cite
  • Documented pattern of patient harm (escalating from anecdote to data)
  • Reader-submitted evidence of a fact we got wrong
  • Provider posting publicly-verifiable data we couldn't independently access (we update upward)

How we filter fake reviews

Most aggregator sites quote raw Trustpilot stars. Raw ratings include fake reviews, incentivized reviews, and review-bombing campaigns. Here's our filter pipeline:

  1. Pull raw data from Trustpilot, BBB, ComplaintsBoard, Sitejabber, Reddit, and Google Business Profile
  2. Exclude reviews younger than 14 days — too easy for brands to seed before launch
  3. Exclude reviewer accounts created within 7 days of review — fake-account signal
  4. Cluster by language patternsusing cosine similarity — flag clusters where 5+ reviews share >80% phrase overlap
  5. Cross-reference IP geolocation (where available) — flag if non-USA IPs concentrated about brand HQ region
  6. Weight 5★ reviews 50% lesswhen account history is < 3 distinct reviews
  7. Reddit sentiment — pull r/Menopause, r/Perimenopause, r/PCOS, r/glp1 for brand mentions; weight by upvote ratio + comment depth

We publish detailed fake-review audits when patterns are egregious. See recent audits.

Source weighting formula

When sources disagree, we weight by source reliability. Higher weight = more influence on final score.

  • FDA labels / peer-reviewed studies weight: 1.0
  • Medical society guidelines (NAMS, ACOG, AACE, ENDO) weight: 0.9
  • Brand-published outcomes data (verified via lookup) weight: 0.7
  • Manual signup test (our staff) weight: 0.8
  • Trustpilot/BBB (post-filter) weight: 0.5
  • Reddit sentiment (post-filter) weight: 0.4
  • Brand marketing copy weight: 0.1

Brand marketing copy gets 0.1 because it's promotional — we don't ignore it, but we anchor heavily to independent data.

Report an error

Found something wrong? Email the editorial team. We respond within 48 hours and publish corrections within 7 days.