openevidence-core-workflow-b
Execute OpenEvidence DeepConsult workflow for comprehensive medical research. Use when implementing deep research synthesis, complex clinical questions, or when physicians need extensive literature review. Trigger with phrases like "openevidence deepconsult", "deep research", "comprehensive evidence", "literature synthesis".
- License 许可
- MIT
- 最近更新
- 2026-05-26
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01 前言 Front Matter
- name
- openevidence-core-workflow-b
- author
- Jeremy Longshore <jeremy@intentsolutions.io>
- license
- MIT
- version
- 1.0.0
- description
- Execute OpenEvidence DeepConsult workflow for comprehensive medical research. Use when implementing deep research synthesis, complex clinical questions, or when physicians need extensive literature review. Trigger with phrases like "openevidence deepconsult", "deep research", "comprehensive evidence", "literature synthesis".
- allowed-tools
- Read, Write, Edit, Bash(npm:*), Grep
- compatible-with
- claude-code, codex, openclaw
03 SKILL.md 正文
OpenEvidence Core Workflow B: DeepConsult Research
Overview
Execute comprehensive medical evidence synthesis using OpenEvidence DeepConsult API. Build multi-source literature reviews, generate evidence summaries with confidence levels, and create structured clinical decision support outputs.
Prerequisites
- OpenEvidence API key with DeepConsult access
- Understanding of medical evidence hierarchies
- Familiarity with clinical question formats (PICO)
- Output template for clinical summaries
Instructions
Step 1: Structure Clinical Questions (PICO Format)
interface ClinicalQuestion {
population: string; // Patient population
intervention: string; // Treatment or exposure
comparison?: string; // Alternative treatment
outcome: string; // Desired outcome
studyType?: string; // RCT, meta-analysis, cohort
}
function formatPICOQuery(q: ClinicalQuestion): string {
let query = `In ${q.population}, does ${q.intervention}`;
if (q.comparison) query += ` compared to ${q.comparison}`;
query += ` improve ${q.outcome}?`;
return query;
}
// Example
const question: ClinicalQuestion = {
population: 'adults with type 2 diabetes',
intervention: 'GLP-1 receptor agonists',
comparison: 'DPP-4 inhibitors',
outcome: 'cardiovascular outcomes',
};
Step 2: Execute DeepConsult Query
const OPENEVIDENCE_API = 'https://api.openevidence.com/v1';
async function deepConsult(query: string, options?: {
maxSources?: number;
evidenceLevel?: 'all' | 'high-quality' | 'rct-only';
recencyYears?: number;
}) {
const response = await fetch(`${OPENEVIDENCE_API}/deepconsult`, {
method: 'POST',
headers: {
'Authorization': `Bearer ${process.env.OPENEVIDENCE_API_KEY}`,
'Content-Type': 'application/json',
},
body: JSON.stringify({
query,
max_sources: options?.maxSources || 20,
evidence_filter: options?.evidenceLevel || 'all',
recency_years: options?.recencyYears || 5,
}),
});
if (!response.ok) throw new Error(`DeepConsult error: ${response.status}`);
return response.json();
}
Step 3: Process and Structure Evidence
interface EvidenceSummary {
question: string;
conclusion: string;
confidenceLevel: 'high' | 'moderate' | 'low' | 'very-low';
keyFindings: string[];
sources: Array<{
title: string;
authors: string;
journal: string;
year: number;
evidenceType: string;
url?: string;
}>;
limitations: string[];
}
function processDeepConsultResponse(response: any): EvidenceSummary {
return {
question: response.query,
conclusion: response.synthesis?.conclusion || '',
confidenceLevel: mapConfidence(response.synthesis?.confidence),
keyFindings: response.synthesis?.key_findings || [],
sources: (response.citations || []).map((c: any) => ({
title: c.title,
authors: c.authors?.join(', ') || 'Unknown',
journal: c.journal || 'Unknown',
year: c.year || 0,
evidenceType: c.study_type || 'Unknown',
url: c.doi ? `https://doi.org/${c.doi}` : undefined,
})),
limitations: response.synthesis?.limitations || [],
};
}
function mapConfidence(score: number): EvidenceSummary['confidenceLevel'] {
if (score >= 0.8) return 'high';
if (score >= 0.6) return 'moderate';
if (score >= 0.4) return 'low';
return 'very-low';
}
Step 4: Generate Clinical Summary Report
function generateReport(summary: EvidenceSummary): string {
const lines = [
`# Evidence Summary`,
``,
`**Question:** ${summary.question}`,
``,
`## Conclusion (Confidence: ${summary.confidenceLevel})`,
summary.conclusion,
``,
`## Key Findings`,
...summary.keyFindings.map((f, i) => `${i + 1}. ${f}`),
``,
`## Sources (${summary.sources.length})`,
...summary.sources.map(s =>
`- ${s.authors} (${s.year}). *${s.title}*. ${s.journal}. [${s.evidenceType}]`
),
``,
`## Limitations`,
...summary.limitations.map(l => `- ${l}`),
];
return lines.join('\n');
}
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| No results returned | Query too narrow | Broaden population or outcome criteria |
| Low confidence score | Insufficient evidence | Expand recency window or evidence filter |
| API timeout | Complex multi-source query | Increase timeout, reduce maxSources |
| Missing citations | Paywalled sources | Use DOI links for source verification |
Examples
Quick Evidence Lookup
const query = formatPICOQuery({
population: 'elderly patients with atrial fibrillation',
intervention: 'direct oral anticoagulants',
comparison: 'warfarin',
outcome: 'stroke prevention',
});
const result = await deepConsult(query, { evidenceLevel: 'high-quality' });
const summary = processDeepConsultResponse(result);
console.log(generateReport(summary));
Resources
Output
- Configuration files or code changes applied to the project
- Validation report confirming correct implementation
- Summary of changes made and their rationale
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