Our Methodology
A transparent look at how StudyLens extracts, assesses, and contextualizes health research.
Extraction Pipeline
Every study goes through a five-step automated pipeline, with each step idempotent and retryable.
Resolve Identifiers
Given a DOI, PMID, or title, we query OpenAlex, Crossref, PubMed E-utilities, and Semantic Scholar to resolve all cross-identifiers and gather maximum metadata.
Check Open-Access Status
Unpaywall API determines whether legal full text is available. We never bypass paywalls or use copyrighted content.
Fetch Full Text
PMC XML is preferred, followed by OA location XML/plain text, then OA HTML, then DOI landing page HTML as a last resort. Each source is parsed for scholarly metadata.
Extract Facts & Quality Flags
Claude AI extracts 28 fact categories (16 core + 12 interrogation) with source citations via structured tool calling. If full text yields zero facts, the pipeline automatically retries with the abstract.
Store with Citations
Results are stored with the study record, facts are bulk-inserted with source offsets, and the coverage badge is assigned based on data depth.
Coverage Badges
Each study receives a badge indicating the depth of data available for analysis.
Gold — facts extracted from full text. Silver — facts from abstract or abstract available with registry data. Bronze — metadata only, limited analysis.
Live Example
Quality Checklist
Seven flags give a quick overview of study methodology and reporting quality.
The checklist assesses peer review status, sample size, control group presence, preregistration, study duration, funding disclosure, and conflict of interest declaration. Each flag is data-driven — no subjective judgment.
Live Example
Quality Checklist
Study Interrogation
13 deeper flags across 4 tiers probe for methodological issues that simple checklists miss.
| Tier | Flags | Examples |
|---|---|---|
| Critical | 3 | Sample size adequacy, p-hacking risk, selective reporting |
| Important | 4 | Effect size, confidence intervals, control group quality, blinding |
| Moderate | 3 | Funding bias, conflict of interest, attrition |
| Informational | 3 | Generalizability, follow-up duration, replication status |
Live Example
Study Interrogation
Caution Meter
A composite score (0-100) aggregates all interrogation flags into a single at-a-glance indicator.
Scores map to four labels: Low Concern (0-15), Some Concerns (16-35), Moderate Concern (36-60), and High Concern (61-100). The meter highlights the top contributing flags.
Live Example
Caution Meter
Caution Meter
Confidence Levels
Each extracted fact carries a confidence indicator based on source quality and extraction certainty.
High — extracted from explicit statement in full text. Medium — inferred from abstract or partial information. Low — derived from metadata or indirect sources.
Live Example
Media Discovery
An automated agent finds and analyzes news coverage of each study.
After extraction completes, an LLM agent generates search queries from study metadata, searches Google News RSS, fetches candidate articles, and validates matches with a confidence threshold above 0.7.
Validated articles are classified by stance: positive, negative, cautious, or neutral. A periodic cron re-runs discovery for recent studies (daily for the last 7 days, weekly for the last 30).
For more on our ethical approach to media analysis, see our language standards.
Meta-Topics
Studies are automatically grouped into LLM-managed research areas.
During extraction, the LLM classifies each study into existing topics or proposes new ones. Topics start in a proposed state and are promoted to active once 3 or more studies are associated.
Topic descriptions, hierarchy, and evidence summaries are all LLM-generated. No manual curation is required — the taxonomy evolves as new studies are added.
Standards Alignment
Our quality assessment draws from established reporting standards.
Questions about our methodology? Get in touch. For our ethical commitments, see Ethics & Transparency.