Not all evidence is created equal.
A single observational study can be confounded by hundreds of variables. Here is the hierarchy we follow, and we always tell you where each claim sits on this ladder.
Confidence ratings, defined.
GRADE (Grading of Recommendations Assessment, Development and Evaluation) was developed by Guyatt et al. and is adopted by the WHO, Cochrane Collaboration, and over 100 health organizations worldwide. It is the international standard for rating how much confidence we can place in a scientific finding. Each rating means something specific: High Certainty means the evidence is so robust that further research is very unlikely to change the conclusion. Very Low Certainty means the true effect may be substantially different from what current studies show.
Further research is very unlikely to change this conclusion. Typically requires multiple well-conducted RCTs with consistent results, adequate sample sizes, low risk of bias, and no detectable publication bias. This is the ceiling. Most health topics do not reach it.
The true effect is probably close to the estimate, but there is a real possibility it could be substantially different. A single well-powered RCT, or a meta-analysis with moderate heterogeneity (I² 25–50%), typically sits here.
Our confidence in the estimate is limited. Observational studies start here by default. Not because they are useless, but because confounding and bias can substantially distort even large, well-run cohort studies.
Very little confidence in the estimate. The true effect is likely substantially different from what current data suggests. Cross-sectional data, case reports, expert opinion, and most animal studies sit here when discussing human applications.
Three tiers. Three different voices.
The language we use to describe a finding is calibrated to the evidence behind it. We will never use the vocabulary of certainty when the evidence only justifies caution — and we will never hide behind vague hedging when the evidence is solid.
What we will never cite as evidence.
These are not judgment calls. They are absolute disqualifiers — evidence types that cannot support a health claim regardless of how they are framed or how prestigious the journal that published them.
Five steps. Zero shortcuts.
From topic selection to publication, every piece of content passes through a rigorous pipeline designed to ensure accuracy and integrity.
AI accelerates. Researchers decide.
Every biological claim on this site goes through human review by people with formal research training. Our process is not just automated quality checks — it includes specialist consultation at the verification stage.
Claims in specialized domains are reviewed by domain experts before publication. We consult researchers and academics with relevant postgraduate training to flag errors, misrepresentations, or misapplied findings.
Our science verifier cross-checks every PMID against PubMed before a post is written. But human consultants go further: they read the full paper, assess methodology, and flag conclusions that exceed what the data supports.
When a consultant or reader identifies an error in published content, we correct it publicly and disclose what changed. Getting it right matters more than appearing infallible.
How we use AI.
We use Claude AI by Anthropic as a research assistant. Here's exactly what that means — and what it doesn't.
Found an error?
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