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parallel-literature-search

在PubMed、Perplexity和您的知识库中进行并行搜索。同时搜索所有来源，并引用综合发现。更快地为临床问题收集证据。

person作者: jakexiaohubgithub
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Parallel Literature Search

All sources at once. This skill searches PubMed, web, and your RAG knowledge base in parallel, then synthesizes the findings into a single coherent summary with citations.

WHAT IT DOES

| Source | What It Searches | Output | |--------|------------------|--------| | PubMed | Academic literature, trials, reviews | PMIDs, abstracts, citations | | Perplexity | Web, recent news, guidelines | Summaries with sources | | RAG (AstraDB) | Your curated knowledge base | Guideline excerpts, textbook refs |

THE DIFFERENCE

| Approach | Sources | Time | Depth | |----------|---------|------|-------| | Sequential search | One at a time | 5+ min | Deeper but slow | | Parallel search | All at once | 30-60 sec | Fast overview | | Manual search | You do it | 20+ min | Variable |

TRIGGERS

Use this skill when you say:

  • "Search for evidence on [topic]"
  • "What does the literature say about [topic]?"
  • "Find research on [topic]"
  • "Quick literature review on [topic]"
  • "Evidence for [clinical question]"

USAGE

In Claude Code (Recommended)

"Parallel search: SGLT2 inhibitors in HFpEF"

"Find all evidence on GLP-1 and cardiovascular outcomes"

"What does literature say about statin discontinuation?"

CLI Mode

# Basic search
python scripts/parallel_search.py --query "SGLT2 inhibitors heart failure"

# Specify sources
python scripts/parallel_search.py --query "GLP-1 cardiovascular" --sources pubmed,perplexity

# Save output
python scripts/parallel_search.py --query "CAC scoring" --output ~/research/

OUTPUT FORMAT

# Literature Search: SGLT2 Inhibitors in HFpEF

**Query:** SGLT2 inhibitors heart failure preserved ejection fraction
**Searched:** 2025-01-01 09:30:45
**Sources:** PubMed, Perplexity, RAG

---

## SYNTHESIS

SGLT2 inhibitors have demonstrated significant benefit in HFpEF based on
EMPEROR-Preserved and DELIVER trials. Key findings:

1. **EMPEROR-Preserved (PMID: 34449189)**: Empagliflozin reduced composite
   endpoint of CV death/HHF by 21% (HR 0.79, 95% CI 0.69-0.90)

2. **DELIVER (PMID: 36027570)**: Dapagliflozin showed 18% reduction in
   worsening HF/CV death (HR 0.82, 95% CI 0.73-0.92)

3. Current guidelines (ACC/AHA 2022) recommend SGLT2i as Class 2a for HFpEF.

---

## PUBMED RESULTS (5 most relevant)

| # | Title | PMID | Year | Type |
|---|-------|------|------|------|
| 1 | Empagliflozin in HFpEF | 34449189 | 2021 | RCT |
| 2 | Dapagliflozin in HFpEF | 36027570 | 2022 | RCT |
| 3 | Meta-analysis SGLT2i HF | 37654321 | 2023 | MA |
| 4 | Real-world SGLT2i outcomes | 38765432 | 2024 | Obs |
| 5 | SGLT2i mechanism review | 39876543 | 2024 | Rev |

---

## WEB RESULTS (Perplexity)

- **ACC 2024 Update**: New data on SGLT2i in cardiorenal syndrome
- **ESC Guidelines 2023**: Updated recommendations for SGLT2i
- **Clinical Practice**: Real-world prescribing patterns

---

## RAG RESULTS (Your Knowledge Base)

- **Braunwald Ch. 27**: Heart failure classification and treatment
- **ACC/AHA HF Guidelines**: Class recommendations for SGLT2i
- **ESC HF Guidelines**: European perspective on SGLT2i use

---

## EVIDENCE QUALITY

| Source | Strength | Notes |
|--------|----------|-------|
| EMPEROR-Preserved | High | Large RCT, well-conducted |
| DELIVER | High | Large RCT, confirmatory |
| Meta-analyses | High | Consistent findings |
| Real-world | Moderate | Observational limitations |

---

## KEY CITATIONS

1. Anker SD, et al. N Engl J Med. 2021;385:1451-1461. (PMID: 34449189)
2. Solomon SD, et al. N Engl J Med. 2022;387:1089-1098. (PMID: 36027570)
3. Vaduganathan M, et al. Lancet. 2022;400:757-767. (Meta-analysis)

---

## GAPS & CONSIDERATIONS

- Limited data in specific HFpEF phenotypes
- Long-term safety data still accumulating
- Indian-specific data limited (consider local studies)

ARCHITECTURE

User Query
     │
     ├──────────────────┬──────────────────┐
     │                  │                  │
     ▼                  ▼                  ▼
[PubMed Agent]   [Perplexity Agent]  [RAG Agent]
     │                  │                  │
     ▼                  ▼                  ▼
  PMIDs &           Web sources       Guideline
  Abstracts         & summaries       excerpts
     │                  │                  │
     └──────────────────┴──────────────────┘
                        │
                        ▼
               [Synthesis Agent]
                        │
                        ▼
              Unified Report with
              Citations & Evidence

INTEGRATION

Works With:

  • quick-topic-researcher - Quick overview
  • deep-researcher - Comprehensive review
  • youtube-script-master - Evidence for scripts
  • cardiology-editorial - Literature for editorials

Feeds Into:

  • Content creation pipeline
  • Video script research
  • Editorial writing
  • Newsletter content

DEPENDENCIES

# Core
anthropic>=0.18.0
python-dotenv>=1.0.0
rich>=13.0.0

# Already have these via your setup
# PubMed MCP - configured in .mcp.json
# Perplexity - via OpenRouter or MCP

API KEYS NEEDED

| Key | Purpose | Status | |-----|---------|--------| | ANTHROPIC_API_KEY | Synthesis | Already have | | NCBI_API_KEY | PubMed (via MCP) | Already have | | PERPLEXITY_API_KEY | Web search | Already have |

HOW CLAUDE SHOULD USE THIS SKILL

When user asks for literature/evidence:

Step 1: Parse the Query

Extract:

  • Main topic
  • Specific aspects (population, intervention, outcome)
  • Time frame (if mentioned)

Step 2: Launch Parallel Searches

# PubMed (via MCP)
pubmed_search_articles(queryTerm="SGLT2 inhibitors heart failure", maxResults=10)

# Perplexity (via MCP or API)
perplexity_ask(messages=[{"role": "user", "content": "Latest evidence on SGLT2 inhibitors in heart failure 2024"}])

# RAG (if available)
# Query AstraDB for relevant guidelines

Step 3: Synthesize Results

Combine findings from all sources into:

  • Key takeaways
  • Evidence quality assessment
  • Complete citation list
  • Gaps and considerations

Step 4: Format Output

Structured report with:

  • Executive synthesis
  • Source-by-source findings
  • Full citations
  • Actionable insights

CLINICAL QUESTION OPTIMIZATION

The skill recognizes PICO format:

| Component | Example | How It's Used | |-----------|---------|---------------| | Patient | "elderly patients with HFpEF" | Filters PubMed | | Intervention | "SGLT2 inhibitors" | Primary search term | | Comparison | "vs placebo" | Narrows to RCTs | | Outcome | "mortality" | Focuses results |

SAMPLE QUERIES

# Basic clinical question
"SGLT2 inhibitors in heart failure"

# PICO format
"In elderly patients with HFpEF, do SGLT2 inhibitors reduce mortality compared to placebo?"

# Specific trial
"What are the key findings from EMPEROR-Preserved?"

# Guideline-focused
"Current ACC/AHA recommendations for SGLT2i in heart failure"

# Comparative
"SGLT2i vs GLP-1 for cardiovascular outcomes in diabetes"

NOTES

  • Speed: Parallel search takes 30-60 seconds vs 5+ minutes sequential
  • Depth: Good for overview, not exhaustive systematic review
  • Citations: Always includes PMIDs for verification
  • Updates: Perplexity provides most recent web data

This skill gives you evidence from multiple sources in under a minute - perfect for content preparation and quick clinical questions.