The evidence behind what’s worth taking.

We start by identifying research that is actually relevant to supplement decisions.

A paper is not treated as useful just because it mentions a nutrient, pathway, biomarker, animal model, or mechanism. The first question is whether the paper contributes human supplement evidence that can reasonably inform an intervention decision.

Published research coverage, reviewed September 8, 2026: our research library contains 255,602 supplementation-related papers. Of these, 8,564 human trials and reviews pass our quality thresholds and have structured extraction used directly in optimization. Our September 2, 2026 Evidence Rating snapshot includes 50,979 results-bearing papers with a numeric rating.

Additionally, we establish a minimum study design quality threshold grounded in our evidence rating.

Not every output is a formal systematic review. The goal is more practical: apply the habits of evidence-based review at product scale.

A study summary is not enough for a supplement decision.

We extract structured facts from papers, including:

• intervention arms
• active ingredients and forms
• sample sizes
• demographics
• health status and baseline context
• dose and duration
• measured endpoints
• comparator type
• timing
• direction of effect
• magnitude where available
• adverse events and attrition where available

We are intentionally conservative during extraction. Missing information stays missing. An unclear dose is not guessed. An unclear comparator is not silently treated as placebo. A broad conclusion is not converted into a stronger claim than the paper supports.

Supplement studies rarely measure the same thing in the same way.

One sleep study might measure sleep latency. Another might measure subjective sleep quality. Another might measure total sleep time, HRV, next-day cognition, or a stress biomarker. Those endpoints can all be relevant, but they are not interchangeable.

We map raw study endpoints into broader groups of outcomes. A central endpoint gets more credit than a weak proxy. A specific outcome gets more credit than a vague wellness-adjacent marker. Evidence is strongest when the measured endpoint closely matches the outcome the user cares about.

This prevents a common supplement-marketing failure mode: treating any related biomarker as proof for a much broader claim.

The methodological reason is straightforward. Research becomes easier to compare when outcomes are measured and reported consistently. COMET's core outcome set work exists for a similar reason: it defines agreed standardized outcomes that should be measured and reported, at minimum, in trials for a specific area of health care.

A supplement result does not mean much without context.

Comparator, dose, timing, and baseline status all change what a finding means. A benefit versus placebo is not the same as a benefit versus baseline. A no-effect result at the wrong dose, after too little time, or in people who already had adequate status should not be treated as proof that an intervention cannot help.

We use those details to avoid treating all positive or negative findings as equal.

Supplement research can easily look stronger than it is.

One paper may report many related endpoints. A review may include trials already represented elsewhere in the evidence base. A combination study may test several ingredients at once, making it hard to know which component drove the effect. Related supplement forms may overlap without being identical.

We group related evidence, track overlap, and discount support when the evidence is correlated or difficult to attribute.

We do not just look for the supplement with the highest apparent upside, the most papers, or the largest headline effect.

More research is useful, but quantity alone is not enough. A heavily studied supplement can still have mixed results, weak endpoints, small effects, poor population fit, or evidence that does not apply well to the outcome being evaluated. A less-studied intervention can sometimes be more relevant if the available human evidence is more consistent, better measured, and more directly tied to goal and focus.

We favor interventions that still look useful after uncertainty is applied.

That means an intervention with one exciting but fragile study may carry less weight than an intervention with smaller, more consistent, better-supported effects. It also means a supplement with many papers does not automatically rise to the top if those papers are indirect, duplicated, low quality, or inconsistent.

Sparse evidence, weak endpoints, immature research areas, high dropout, unclear comparators, overlapping reviews, indirect populations, and possible publication bias all reduce confidence.

This is especially important in supplement research, where "more studied" does not always mean "more useful." The question is not how much research exists in total. The question is what the research shows, how consistent it is, how directly it maps to the outcome, and whether the expected benefit still holds up after uncertainty is accounted for.

The evidence does not point to the same supplements for everyone.

A study in older adults, deficient participants, trained athletes, healthy adults, or people with a specific condition does not mean the same thing for every user. Stakkaz matches evidence against your context, including your goal, current stack, doses, age, sex, medications, conditions, allergens, diet, and constraints, so evidence is used where it is most applicable and discounted where it is less relevant.

That is how Stakkaz moves from all the evidence to your evidence.

What the scores mean

The scores and meters measure opportunity, not effect. Goal Support reflects how much relevant, realistically available support your stack captures: support from interventions with positive human research for your goal, in your context.

Where there is less opportunity, because fewer interventions have that research, or because interactions, safety or your restrictions would limit what an intervention could realize, that is accounted for. What you cannot take is not held against you, and a strong score never depends on an ever-larger stack.

The analysis cannot know that adding another intervention for the same outcome will have the same effect. It shows how much of the research-backed opportunity your stack already captures, and what is left.

A higher score does not necessarily mean a better stack for you. It shows how much of the available support your stack captures; how far to go is your choice, weighed against what matters to you, what you are willing to spend and which trade-offs you want to make.