AGENT SKILL

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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