tooluniverse-clinical-guidelines

# Clinical Guidelines Search & Retrieval

## Guideline Hierarchy

Not all guidelines carry equal weight. Evaluate sources in this order:

1. **NICE and WHO** — Evidence-graded, regularly updated, rigorous systematic review process. NICE guidelines include explicit recommendation strength (e.g., "offer" vs "consider").
2. **Society guidelines (AHA, ADA, NCCN, SIGN)** — Expert-consensus panels within a specialty. May lag behind the latest evidence by 1-3 years. Strong within their domain but narrower scope.
3. **Aggregator databases (GIN, TRIP, OpenAlex)** — Index guidelines from multiple societies. Good for breadth and discovery, but you must verify the original source.
4. **Literature databases (PubMed, EuropePMC)** — Return guideline-related publications, not curated guideline text. Useful as a fallback, not a primary source.

**Always check publication date.** A 2015 guideline may be superseded by a 2024 update. When presenting results, include the year prominently and note if newer guidance may exist.

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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

## Search Strategy

### Step 1: Start Narrow, Then Broaden

1. Search the **condition name + "guideline"** in NICE, TRIP, and GIN simultaneously (parallel calls).
2. If the question targets a specialty, add the society tool: AHA for cardiology, ADA for diabetes, NCCN for oncology, CPIC for pharmacogenomics.
3. If initial searches return nothing, broaden to the disease category (e.g., "heart failure" instead of "HFpEF with SGLT2 inhibitors").
4. If society-specific tools fail, fall back to PubMed/EuropePMC with `[condition] guideline [year]`.

### Step 2: Search at Least 3 Sources

Always query a minimum of 3 databases to catch guidelines that one source may miss. Prioritize: **NICE > GIN > TRIP > Society-specific > Literature databases**.

### Step 3: Retrieve Full Text When Available

After identifying relevant guidelines from search results, use full-text tools to get recommendation details before synthesizing.

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## Diagnostic Test Selection Reasoning

When a clinical question asks "which test should be ordered?" or "what is the most appropriate next diagnostic step?", apply this reasoning framework BEFORE searching guidelines.

### Step 1: What Is the Clinical Question Actually Asking?

Diagnostic tests serve different purposes. Identify which one the question demands:
- **Screening**: Detect disease in an asymptomatic population. Prioritize SENSITIVITY (minimize false negatives). Example: ANA for SLE screening.
- **Confirmation**: Confirm a suspected diagnosis. Prioritize SPECIFICITY (minimize false positives). Example: anti-dsDNA or anti-Smith for SLE confirmation.
- **Differentiation**: Distinguish between two diagnoses that look similar. Choose the test that is POSITIVE in one and NEGATIVE in the other. Example: ASO titers to distinguish PSGN from SLE nephritis (both have low complement and hematuria, but only PSGN has elevated ASO).
- **Staging/Prognosis**: Determine disease severity after diagnosis is established. Example: renal biopsy ISN/RPS class for lupus nephritis.
- **Monitoring**: Track response to treatment. Example: anti-dsDNA titers and complement levels in SLE.

### Step 2: Match the Test to the Diagnostic Gap

Ask: "What piece of information am I MISSING that would change management?"

### Step 3: Sensitivity vs Specificity Decision Matrix

| Scenario | Prioritize | Reasoning |
|----------|-----------|-----------|
| Ruling OUT a dangerous condition | High sensitivity | A negative result reliably excludes the disease |
| Confirming before invasive treatment | High specificity | A positive result reliably confirms the disease |
| Differentiating two similar conditions | Test unique to one | Choose marker present in condition A but absent in condition B |
| Emergency with life-threatening DDx | Fastest available test | Speed trumps perfect accuracy in acute settings |

### Step 4: Common Test Selection Pitfalls

1. **Ordering a test that is positive in BOTH conditions on the differential** — C3/C4 is low in both SLE and PSGN; it does not differentiate. Always ask: "Would this test result change my differential?"
2. **Ordering a screening test when a confirmatory test is needed** — ANA is sensitive but not specific for SLE. If you already suspect SLE, order anti-dsDNA or anti-Smith (specific).
3. **Skipping the simple test for the exotic one** — ASO titers are cheap and fast. Do not jump to renal biopsy before checking whether streptococcal infection explains the presentation.
4. **Forgetting temporal context** — PSGN complement normalizes in 6-8 weeks; SLE complement stays persistently low. A single complement level is less useful than a trend.
5. **Ignoring pre-test probability** — A test with 95% specificity still has a 50% false-positive rate if the pre-test probability is only 5%. Consider the clinical picture first.

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## Lab Test Interpretation Strategy

- Always consider **pre-test probability** before interpreting any result. A positive test in a low-prevalence population has a high false-positive rate regardless of test accuracy.
- **SnNOut**: A highly **Se**nsitive test, when **N**egative, rules **Out** the disease. Use sensitive tests for screening.
- **SpPIn**: A highly **Sp**ecific test, when **P**ositive, rules **In** the disease. Use specific tests for confirmation.
- For **conflicting results** (e.g., one test positive, another negative): repeat the discordant test, order a different confirmatory test, or re-evaluate the clinical picture and pre-test probability.
- **Likelihood ratios** trump sensitivity/specificity alone. LR+ >10 or LR- <0.1 meaningfully shift post-test probability.

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## Surgical Decision Mak