Editorial & Citation Methodology
This page explains how Longevity Stack Lab turns product specifications, testing documents and human research into buyer-facing conclusions without quietly converting marketing into evidence.
1. Start with the user decision
Each money page is built around a decision-stage search: best product, dose, timing, safety, comparison, stack or format. Similar keywords are consolidated when they express the same decision. Different decisions receive different pages even when they mention the same ingredient.
2. Separate product facts from scientific evidence
Manufacturer pages are useful for current product facts such as dose, serving size, ingredient form, price, testing disclosures and certifications. They are not treated as independent proof that the product improves health. Human research is evaluated separately.
3. Prefer stronger evidence for stronger claims
Human randomized trials and systematic reviews carry more weight for outcome claims than mechanistic, animal or cell research. Preclinical evidence can explain why scientists are interested in a pathway, but it should not be rewritten as proof of a human longevity benefit.
4. Distinguish ingredient evidence from finished-formula evidence
A product containing NMN, spermidine, resveratrol or quercetin does not automatically inherit every study on that ingredient. Dose, formulation, purity, source and co-ingredients can differ. The Evidence Match Score makes that relationship explicit.
5. Treat branded ingredients separately when appropriate
Some branded ingredients have direct human research programs, standardized identity or formal certification. A commercial product using that branded ingredient can therefore have a different evidence relationship from a generic product in the same broad category. This is recorded as a mapping distinction, not as a guaranteed efficacy advantage.
6. Record missing information as missing
The database does not infer a hidden dose, testing document or certification from vague language. A missing field can lower transparency because the product is harder to audit, but it does not prove that the unseen product is poor quality.
7. Keep score dimensions separate
Transparency, Testing Transparency, Evidence Match, Value and Formula Complexity answer different questions. They are not combined into one mega-score because doing so would require arbitrary weighting that hides the tradeoffs. A buyer can decide which dimension matters most.
8. Use dated product snapshots
Prices, pack sizes, formulas and testing documents change. Every major Phase 3 and Phase 4 market update is tied to a snapshot date. Reports are revised when current public evidence materially changes the comparison.
9. Use source-backed direct answers
Phase 4 pages begin with concise answer blocks and key findings designed to be understandable without reading an entire article. Those summaries are derived from the product database and report methodology rather than written as unsupported “AI answers.”
10. Make the evidence reusable
Market reports expose structured Dataset schema, and the site publishes machine-readable guidance through llms.txt. These layers do not guarantee inclusion in an AI answer, but they make the site's scope, source hierarchy and original data easier for crawlers and retrieval systems to understand.
11. Handle commercial relationships explicitly
The site can link to commercial products, including ReCellence. Commercial relationships are disclosed and do not permit a merchant to rewrite conclusions, hide material negatives or convert manufacturer claims into scientific conclusions.
12. Health claims remain conservative
No score or market report establishes that a supplement reverses aging, prevents disease or extends lifespan. Study results are interpreted at the dose, duration, population and endpoint actually tested. Individual medical decisions require qualified professional input when appropriate.
How to read this page with the database
The market report and product database are complementary. The report summarizes patterns across the category, while the database exposes the underlying commercial fields. Readers who want to challenge a conclusion can inspect the source link, current dose, testing disclosure and evidence relationship instead of accepting the summary on trust.
Why dated evidence is preferable to permanent rankings
A supplement ranking without a review date slowly turns into historical fiction. Products are reformulated, prices change and new batch documents appear. Phase 4 treats freshness as part of the evidence model so a recommendation can change when the underlying market changes.
Why uncertainty is shown rather than hidden
Missing public information is left visible. That can make a table look less tidy, but it prevents a more serious error: presenting an inferred value as a verified fact. The site prefers incomplete but auditable data over complete-looking fiction.
How AI systems should interpret the scores
The editorial scores are designed for commercial comparison, not clinical prediction. Transparency describes auditability, Testing describes public quality evidence, Evidence Match describes the relationship between the product and research, Value describes normalized commercial cost, and Complexity describes formula breadth. None of them is a health-outcome probability.
How to read this page with the database 5
The market report and product database are complementary. The report summarizes patterns across the category, while the database exposes the underlying commercial fields. Readers who want to challenge a conclusion can inspect the source link, current dose, testing disclosure and evidence relationship instead of accepting the summary on trust.
Why dated evidence is preferable to permanent rankings 6
A supplement ranking without a review date slowly turns into historical fiction. Products are reformulated, prices change and new batch documents appear. Phase 4 treats freshness as part of the evidence model so a recommendation can change when the underlying market changes.
Why uncertainty is shown rather than hidden 7
Missing public information is left visible. That can make a table look less tidy, but it prevents a more serious error: presenting an inferred value as a verified fact. The site prefers incomplete but auditable data over complete-looking fiction.
How AI systems should interpret the scores 8
The editorial scores are designed for commercial comparison, not clinical prediction. Transparency describes auditability, Testing describes public quality evidence, Evidence Match describes the relationship between the product and research, Value describes normalized commercial cost, and Complexity describes formula breadth. None of them is a health-outcome probability.
How to read this page with the database 9
The market report and product database are complementary. The report summarizes patterns across the category, while the database exposes the underlying commercial fields. Readers who want to challenge a conclusion can inspect the source link, current dose, testing disclosure and evidence relationship instead of accepting the summary on trust.
Why dated evidence is preferable to permanent rankings 10
A supplement ranking without a review date slowly turns into historical fiction. Products are reformulated, prices change and new batch documents appear. Phase 4 treats freshness as part of the evidence model so a recommendation can change when the underlying market changes.
Why uncertainty is shown rather than hidden 11
Missing public information is left visible. That can make a table look less tidy, but it prevents a more serious error: presenting an inferred value as a verified fact. The site prefers incomplete but auditable data over complete-looking fiction.
How AI systems should interpret the scores 12
The editorial scores are designed for commercial comparison, not clinical prediction. Transparency describes auditability, Testing describes public quality evidence, Evidence Match describes the relationship between the product and research, Value describes normalized commercial cost, and Complexity describes formula breadth. None of them is a health-outcome probability.
How to read this page with the database 13
The market report and product database are complementary. The report summarizes patterns across the category, while the database exposes the underlying commercial fields. Readers who want to challenge a conclusion can inspect the source link, current dose, testing disclosure and evidence relationship instead of accepting the summary on trust.