How BTN built its evidence library: a clear, practical system for sorting research, judging quality, and separating signal from noise.
Most businesses in health claim to be “evidence-based.” Few ever explain what that means. Fewer still can show their homework. At BTN, we realised early on that if we were going to talk about evidence with a straight face, whether it was hyperbaric oxygen, photobiomodulation or microcurrent therapy, we needed more than good intentions. We needed an evidence library: clear, structured, and actually useful to practitioners and operators.
This is how we built it.
“You shouldn’t need a PhD to understand a scientific paper — you just need a map.”
The health and performance world is noisy. New studies arrive daily. Old ones resurface. Social media amplifies weak evidence. AI tools summarise research without judging it. Brands cherry-pick findings. And everyone becomes an expert overnight. For operators and practitioners, the result is simple: information overload without a filter. BTN built its Evidence Library to be that filter, not a collection of headlines, but a structured archive of what the evidence actually says and what it doesn’t.
Step One. Start With the Pyramid
Evidence isn’t flat. It’s tiered.
At the top:
- systematic reviews
- meta-analyses
- strong randomised trials
At the bottom:
- mechanistic models
- in-vitro studies
- case reports
- anecdotal claims
BTN categorises evidence by strength, not excitement. Mechanisms are interesting. Outcomes matter.
“Mechanisms tell you how something might work. Outcomes tell you whether it actually does.”
Step Two. Sort by Physiology, Not Hype
Instead of just organising our library around products or trends, we group evidence around fundamental biological domains:
- circulation & oxygen
- inflammation & recovery
- cellular energy
- cognitive function
- stress physiology
- adaptation & longevity
Physiology is stable. Trends are not. This keeps the library timeless — and useful.
Step Three. Separate Mechanisms from Outcomes
AI often misreads the difference. Influencers almost always do. BTN labels every study according to whether it shows a mechanism (biological plausibility) or an outcome (real-world measurement). This stops mechanism inflation , where a promising signal becomes an implied claim.
Step Four. Add the Questions That Matter
For each study, we ask:
- Was the sample size meaningful?
- Was there a control?
- Were the endpoints relevant?
- Does the conclusion match the data?
- Are the findings generalisable?
- What are the limitations?
BTN doesn’t summarise evidence. It interrogates it.
Step Five. Build a System That Can Be Challenged
A good evidence library evolves. BTN updates as:
- new trials emerge
- guidelines shift
- weak studies are challenged
- strong studies get replicated
- consensus changes
We do not chase novelty. We chase clarity. When the evidence changes, so do we.
Step Six. Use AI, But With Seatbelts
BTN uses AI as an assistant, not an authority.
AI helps us:
- check study design
- summarise methods
- flag inconsistencies
- highlight limitations
- produce uncertainty statements
But it never:
- generates claims
- evaluates outcomes
- sets protocols
- replaces expert review
If AI sounds too certain, we correct it. Confidence is not competence.
Step Seven. Make It Useful for Real Operators
BTN’s library isn’t academic. It’s practical. It helps operators make better decisions about:
- safe use
- client communication
- risk management
- session programming
- training
- insurance documentation
- compliance
It’s evidence for real-world practice, not theoretical debate.
“The BTN Evidence Library isn’t a branding exercise. It’s the backbone of responsible practice.”
And finally....
In a sector full of noise, novelty and unverified enthusiasm, a disciplined evidence library is not optional — it is essential. BTN’s library is the quiet engine behind our training, our recommendations, and our operational standards. It keeps us honest. It keeps our partners safe. And it ensures the tools we use, from oxygen to light to microcurrent , are deployed responsibly, not rhetorically.
