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Antigravity Agent
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---
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id: wiki-2026-0508-search-methodology
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title: Search Methodology
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Systematic Search, Literature Search, Research Methodology]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.88
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verification_status: applied
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tags: [research, methodology, prisma, systematic-review, literature-search]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: none
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---
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# Search Methodology
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## 매 한 줄
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> **"매 reproducible literature search — define question, query strategy, screen, extract, synthesize"**. PRISMA 2020 매 standard for systematic reviews. 매 2026 update: AI-augmented (Elicit, Consensus, Undermind) + traditional database search 매 hybrid.
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## 매 핵심
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### 매 Research question framing
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- **PICO** (clinical): Population, Intervention, Comparator, Outcome.
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- **PEO** (qualitative): Population, Exposure, Outcome.
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- **SPIDER** (mixed methods): Sample, Phenomenon, Design, Eval, Research-type.
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### 매 PRISMA 2020 flow
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1. **Identification**: 매 records from databases + registers + other.
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2. **Screening**: 매 title/abstract → eligible.
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3. **Eligibility**: 매 full-text review.
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4. **Included**: 매 final corpus → synthesis.
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### 매 Database strategy
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- **Medical**: PubMed, EMBASE, Cochrane CENTRAL.
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- **CS**: Google Scholar, Semantic Scholar, ACM/IEEE/arXiv.
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- **Social**: Web of Science, Scopus, PsycINFO.
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- 매 매 multiple databases 매 essential — 매 single source 매 missing 30-50%.
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### 매 Query construction
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- Boolean: AND, OR, NOT.
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- 매 controlled vocabulary: MeSH, Emtree, ACM CCS.
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- 매 truncation: `child*` matches child, children.
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- 매 proximity: `"machine learning" NEAR/3 medicine`.
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### 매 AI-augmented (2024-2026)
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- **Elicit**: 매 question → relevant papers + extraction.
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- **Consensus**: 매 yes/no claim verification.
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- **Undermind**: 매 deep search agents.
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- **OpenAlex API**: 매 250M scholarly works open.
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### 매 응용
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1. Systematic review / meta-analysis.
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2. Tech due diligence.
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3. PhD literature review.
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4. Patent landscape analysis.
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## 💻 패턴
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### Boolean query construction
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```python
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from itertools import product
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terms = {
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"concept_a": ["machine learning", "ML", "deep learning"],
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"concept_b": ["medical imaging", "radiology", "diagnostic imaging"],
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"concept_c": ["systematic review", "meta-analysis"],
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}
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def build_query(terms):
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blocks = []
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for concept, alts in terms.items():
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block = "(" + " OR ".join(f'"{t}"' for t in alts) + ")"
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blocks.append(block)
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return " AND ".join(blocks)
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print(build_query(terms))
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# ("machine learning" OR "ML" OR "deep learning") AND ("medical imaging" ...) AND ...
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```
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### PubMed E-utilities
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```python
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import requests
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def pubmed_search(query, max_results=200):
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base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"
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r = requests.get(f"{base}/esearch.fcgi", params={
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"db": "pubmed", "term": query, "retmax": max_results, "retmode": "json"
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})
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pmids = r.json()["esearchresult"]["idlist"]
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r2 = requests.get(f"{base}/esummary.fcgi", params={
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"db": "pubmed", "id": ",".join(pmids), "retmode": "json"
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})
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return r2.json()["result"]
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```
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### Semantic Scholar API
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```python
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def s2_search(query, limit=100):
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url = "https://api.semanticscholar.org/graph/v1/paper/search"
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fields = "title,abstract,authors,year,citationCount,openAccessPdf"
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r = requests.get(url, params={"query": query, "limit": limit, "fields": fields})
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return r.json()["data"]
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```
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### Deduplication
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```python
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from rapidfuzz import fuzz
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def dedupe(records):
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unique = []
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seen_titles = []
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for r in records:
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title = r["title"].lower().strip()
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if any(fuzz.ratio(title, t) > 92 for t in seen_titles):
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continue
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seen_titles.append(title)
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unique.append(r)
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return unique
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```
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### Screening with LLM (title+abstract)
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```python
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from anthropic import Anthropic
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client = Anthropic()
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def llm_screen(record, inclusion_criteria):
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prompt = f"""Inclusion criteria: {inclusion_criteria}
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Title: {record['title']}
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Abstract: {record['abstract']}
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Decision (INCLUDE / EXCLUDE / UNSURE) + 1-line reason:"""
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r = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=100,
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messages=[{"role": "user", "content": prompt}],
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)
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return r.content[0].text
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# 매 always 매 human verify UNSURE + sample of INCLUDE/EXCLUDE.
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```
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### PRISMA flow tracking
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```python
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class PRISMA:
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def __init__(self):
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self.counts = {
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"identified_db": 0, "identified_reg": 0, "identified_other": 0,
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"duplicates": 0, "screened": 0, "excluded_screen": 0,
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"fulltext_sought": 0, "fulltext_unavailable": 0,
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"fulltext_assessed": 0, "excluded_eligibility": {},
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"included": 0,
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}
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def render(self):
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for k, v in self.counts.items():
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print(f"{k}: {v}")
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```
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### Forward / backward citation chasing
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```python
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def snowball(seed_dois, depth=1):
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frontier = set(seed_dois)
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found = set()
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for _ in range(depth):
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new = set()
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for doi in frontier:
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refs = s2_get_references(doi)
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cites = s2_get_citations(doi)
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new.update(refs + cites)
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found.update(frontier)
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frontier = new - found
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return found
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Cochrane systematic review | 매 PRISMA 2020 + 2-reviewer double screen |
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| Tech scouting | 매 AI tools (Elicit, Consensus) + Semantic Scholar |
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| Patent search | 매 EPO Espacenet + PatentScope |
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| Quick lit review | 매 Google Scholar + snowball |
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| AI-augmented full review | 매 LLM screen + 100% human verify |
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**기본값**: 매 PRISMA 2020 + Boolean across ≥3 databases + LLM-assist screening + human verification.
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## 🔗 Graph
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- 부모: [[Research Methods]]
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- Adjacent: [[Bibliometrics]] · [[Citation Analysis]]
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## 🤖 LLM 활용
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**언제**: 매 large-corpus screening (10k+ titles), 매 extraction template fill, 매 query expansion.
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**언제 X**: 매 final inclusion decision (매 always human), 매 citation accuracy claim (매 hallucination risk).
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## ❌ 안티패턴
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- **Single database**: 매 30-50% missing.
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- **No protocol**: 매 publication bias 매 invisible.
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- **Single reviewer**: 매 ≥2 with kappa agreement.
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- **LLM-only screening**: 매 hallucination + bias 매 verify 100%.
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- **No PRISMA flow**: 매 unreproducible.
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## 🧪 검증 / 중복
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- Verified (PRISMA 2020 statement, Cochrane Handbook v6.4).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — PRISMA, Boolean, AI-augmented tools |
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