# Interpreting Results

This section explains how to interpret EVd3x outputs without over-claiming.

## 1) Read order for reliable interpretation

1. Confirm context in **Summary** (single node vs collection/system).
2. Check **EV Evidence** strength and provenance.
3. Use **Pathway** for functional framing.
4. Use **Disease** for translational association context.
5. Use **Cell Types / L-R / PPI** for mechanistic depth.

## 2) Direct vs target-derived context (critical for miRNA)

For miRNA-focused analyses, pathway/disease context can include target-derived signals from linked mRNA targets.

Interpretation rule:
- Direct evidence = molecule-linked source rows.
- Target-derived evidence = inferred through target mRNA relationships.

Both are useful, but report them distinctly when writing conclusions.

## 3) Dominant summaries vs full tables

Dominant summary metrics are speed-first by design and may apply caps or prioritization (for example, top high-confidence targets). They help with first-pass interpretation.

For full analysis:
- open the dedicated tab table,
- export full rows,
- validate whether summary patterns hold at full scope.

## 4) Score interpretation guardrails

### Pathway significance
- Use p-value and q-value together.
- Compare only inside the same filter state.

### Disease support
- Higher support/score indicates stronger association context, not causality.
- Check source breadth and publication context.

### Communication/PPI
- Treat as mechanistic hypothesis support.
- Record thresholds and active caps when reporting.

## 5) Research reporting language

Prefer:
- "associated with"
- "supported by"
- "consistent with"

Avoid:
- causal claims unless source evidence is explicitly causal.

## 6) Practical interpretation template

When summarizing a finding, include:
- query and mode,
- strongest EV evidence signal,
- top pathway/disease context,
- caveat (direct vs target-derived, cap/filter effects),
- suggested next validation step.