| name | literature-review |
| description | Academic literature review workflow for research topic selection. Use when user asks to do literature survey, research topic exploration, or systematic review for a specific field. Triggers include: "literature review for [field]", "research topic selection", "help me design a search strategy", "systematic literature search", or when user describes a multi-step research workflow involving database searching, relevance assessment, journal filtering, and topic analysis. |
Literature Review Workflow
A systematic workflow for academic literature survey and research topic selection.
Workflow Overview
1. Research Background -> Generate Search Query
2. Execute Search (PubMed/Scopus/Semantic Scholar)
3. Relevance Assessment (50% threshold)
4. Query Optimization (if needed)
5. Journal Stratification & Filtering
6. Deep Analysis & Topic Mining
Step 1: Generate Search Query
Prompt Template
I need to conduct a literature review for the "[RESEARCH_FIELD]" field to select a research direction. Please design an advanced search query for Scopus/PubMed.
Requirements:
- Include all naming variants
- Use TITLE-ABS-KEY (Scopus) or [Title/Abstract] (PubMed) for field-level search
- Exclude irrelevant literature using TITLE-level exclusion
Naming Variant Discovery
Before generating query, identify all relevant terms:
| Source | Method |
|---|
| English full names | Standard terminology |
| Abbreviations | Common acronyms (caution: may cause false positives) |
| Variant spellings | Different spellings (e.g., "Crohn's" vs "Crohn") |
| Subtypes | Disease subtypes, related conditions |
Query Templates
Scopus:
TITLE-ABS-KEY("term1" OR "term2" OR "term3") AND NOT TITLE("exclude1" OR "exclude2")
PubMed:
("term1"[Title/Abstract] OR "term2"[Title/Abstract]) NOT ("exclude1"[Title] OR "exclude2"[Title])
Common Exclusions
| Exclusion | Reason |
|---|
irritable bowel syndrome / IBS | Common confusion |
case report | Low evidence level |
case presentation | Low evidence level |
Field-specific: gynecolog, oncolog | Exclude non-target specialties |
Step 2: Execute Search
Database Selection
| Database | Best For |
|---|
| PubMed | Biomedical/clinical research |
| Scopus | Comprehensive coverage |
| Semantic Scholar | AI-powered, but API rate limited |
Search Execution via Browser
- Navigate to database URL with encoded query
- Apply filters if needed (publication date, article type)
- Capture total results and first 10-20 papers
PubMed URL Template
https://pubmed.ncbi.nlm.nih.gov/?term=ENCODED_QUERY&sort=date
Step 3: Relevance Assessment
Assessment Criteria
For top 10-20 papers (sorted by date, newest first):
| Score | Criteria |
|---|
| Directly relevant | Core topic, primary focus |
| Relevant | Significant discussion of topic |
| Marginally relevant | Mentioned in passing |
| Not relevant | Different field, wrong context |
Pass Threshold
- 50% match rate (e.g., 5/10 or 10/20 relevant or higher)
- If failed, proceed to query optimization
Assessment Prompt
Evaluate the following [N] papers for relevance to [RESEARCH_FIELD]:
[LIST OF TITLES]
Rate each as: Directly relevant / Relevant / Marginally relevant / Not relevant
Calculate match rate and recommend next steps.
Step 4: Query Optimization
If Match Rate < 50%
Analyze why irrelevant papers were retrieved:
| Problem | Solution |
|---|
| Term too broad | Remove or qualify the term |
| Missing exclusions | Add exclusion terms to TITLE filter |
| False positives from abbreviations | Remove abbreviation, use full terms only |
| Non-target fields appearing | Add field-specific exclusions |
Optimization Prompt
The search query returned these papers but many are irrelevant:
[LIST OF IRRELEVANT PAPERS WITH TITLES]
Analyze why each irrelevant paper was retrieved and propose specific query modifications.
Iterate
- Modify query based on analysis
- Re-run search
- Re-assess match rate
- Repeat until >= 50% match rate achieved
Step 5: Journal Stratification & Filtering
Journal Quality Tiers
| Tier | IF Range | Characteristics |
|---|
| Top | >10 | Field-leading, high impact |
| High | 5-10 | Strong reputation |
| Medium | 3-5 | Solid, specialized |
| Standard | <3 | General, regional |
Field-Specific Top Journals
For each research field, identify the top journals:
Example (Gastroenterology):
- Lancet Gastroenterology & Hepatology (IF ~45)
- Gastroenterology (IF ~29)
- Gut (IF ~24)
- Journal of Crohn's and Colitis (IF ~10)
Filtering Strategy
- Identify top 5-10 journals in the field
- Filter results to focus on high-quality sources
- Use for subsequent deep analysis
Step 6: Deep Analysis & Topic Mining
Analysis Framework
1. Dynamic Topic Evolution
| Category | Indicators |
|---|
| Evergreen (stable/growing) | 5-year consistent publication, steady citations |
| Emerging (1-2 years) | Rapid growth, new methodology/technology |
| Declining (saturated) | Decreasing publications, well-established knowledge |
2. Journal Preferences
Analyze top journals' editorial preferences:
| Journal | Preference |
|---|
| Mechanism-focused | Basic research, pathways |
| Clinical-focused | RCTs, real-world evidence |
| Methods-focused | New techniques, AI applications |
3. High-Frequency Keyword Combinations
Identify patterns:
- Method + Disease subtype
- Population + Intervention
- Biomarker + Outcome
4. Research Opportunity Mining
Overcrowded areas: Avoid (high competition, limited novelty)
Promising directions (by difficulty):
| Level | Characteristics | Risk/Reward |
|---|
| Basic | Survey, simple analysis | Low risk, modest contribution |
| Medium | Cohort study, method application | Moderate risk, good contribution |
| Advanced | Novel mechanism, new intervention | High risk, high impact potential |
Analysis Output Format
## Topic Evolution Analysis
- Evergreen topics: [list with rationale]
- Emerging topics: [list with growth indicators]
- Declining topics: [list with saturation evidence]
## Journal Preferences
[journal-by-journal analysis]
## Keyword Combinations
[high-frequency patterns]
## Research Opportunities
- Overcrowded: [areas to avoid]
- Promising: [5 directions with difficulty levels]
Quick Reference: Workflow Checklist
Common Pitfalls
| Pitfall | Solution |
|---|
| Query too narrow | Include synonyms, check spelling variants |
| Query too broad | Add exclusions, use phrase searching |
| Missing key terms | Consult MeSH terms, field experts |
| Irrelevant results | Analyze false positives, add exclusions |
| Low-quality sources | Apply journal filters, check impact factors |
