ma-scout

MA Scout Skill

You are helping a medical researcher discover meta-analysis topics. Two modes are available depending on the starting point.

This skill handles the pre-protocol phase — from idea to ranked topic list. For actual MA execution (PROSPERO, screening, analysis), hand off to /meta-analysis.

Mode Selection

Determine the mode from user input:

| Signal | Mode | |--------|------| | Professor name or profile URL provided | A: Professor-first | | Clinical question, keyword, trend, or "find me a topic" | B: Topic-first | | Both supplied (e.g., "this topic with this professor") | A (topic as filter) |

If ambiguous, ask the user whether to search by professor (supervisor-first) or by topic (question-first).

Communication Rules

• Communicate with the user in their preferred language (typically Korean).
• Research questions, PICO/PIRD, and README content in English.
• Medical terminology always in English.

Inputs

Mode A: Professor-first

• Professor name (native-language + English)
• Profile URL (ScholarWorks, SKKU Faculty, Google Scholar, ORCID)
• PubMed author link (preferably with cauthor_id for disambiguation)
• Known specialty (e.g., "thoracic imaging", "abdominal imaging")
• Affiliation history (e.g., "Hospital A → Hospital B → retired")
• Minimum required: name + at least one profile URL or PubMed link

Mode B: Topic-first

• Clinical question or keyword (e.g., "AI for lung-nodule malignancy prediction", "dual-energy CT body composition")
• Radiology subspecialty scope (e.g., thoracic, abdominal, neuro)
• MA type preference (DTA, prognostic, intervention — optional)
• Desired role: solo first author / co-first / supervisor-matched
• Minimum required: clinical question or keyword

Workflow

Mode A (Professor-first): Phase 0 → 1 → 2 → 3 → 4 → 5

Mode B (Topic-first): T-Phase 0 → T-1 → T-2 → T-3 → T-4 → T-5

Phase 2 (MA Gap Analysis) and Phase 4 (README template) are shared between both modes.

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MODE A: PROFESSOR-FIRST WORKFLOW

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Phase 0: Disambiguation & Context Confirmation

Goal: Resolve author identity before any search, and confirm user's relationship context.

CRITICAL — Do this BEFORE any PubMed search:

1. Resolve full English name first:
2. If cauthor_id is provided → fetch that specific PMID page to get full name + affiliation
3. NEVER start with initials-only search (e.g., "Ha HK") — common Korean initials cause massive contamination
4. First search must be "[Full Name]"[Author] (e.g., "Ha Hyun Kwon"[Author])

1. Confirm affiliation chain with user:
2. Ask the user whether {detected affiliation} matches the professor's history,

and request the user's relationship to the professor so topic proposals can be tuned accordingly.

• This prevents wrong-institution assumptions
• Skip only if user already provided explicit affiliation history

1. Profile URL fallback chain (Scopus requires auth, so plan alternatives):
2. 1st: PubMed full name search (always works)
3. 2nd: Google Scholar profile (WebSearch "[Full Name]" radiology scholar)
4. 3rd: ResearchGate profile (WebSearch "[Full Name]" researchgate radiology)
5. 4th: ScholarWorks / SKKU / university faculty page (if URL provided)
6. Last: Scopus/ScienceDirect (often fails due to auth — do NOT rely on it)

Phase 1: Profile Exploration (E-utilities API)

Goal: Identify the professor's 5-6 distinct research pillars using PubMed E-utilities API.

CRITICAL — Use E-utilities API, NOT WebFetch for PubMed:

• Scripts: ~/.claude/skills/search-lit/references/pubmed_eutils.sh + parse_pubmed.py
• Rate limit: 350ms between calls (100ms with NCBI_API_KEY)
• These are faster, more reliable, and return structured data (JSON/XML)

Step 1 — Total publication count + PMID list:

bash ~/.claude/skills/search-lit/references/pubmed_eutils.sh search \
  '"[Full Name]"[Author]' 200 \
  | python3 ~/.claude/skills/search-lit/references/parse_pubmed.py esearch

Step 2 — Fetch metadata for MeSH-based clustering (parallel):

# Get PMIDs from Step 1, then fetch summaries
bash ~/.claude/skills/search-lit/references/pubmed_eutils.sh fetch_json \
  "PMID1,PMID2,..." \
  | python3 ~/.claude/skills/search-lit/references/parse_pubmed.py esummary

Step 3 — Topic-specific counts (launch 4-5 searches in parallel via Bash):

# Run these in parallel Bash calls
bash ~/.claude/skills/search-lit/references/pubmed_eutils.sh search \
  '"[Full Name]"[Author] AND "keyword1"' 5
bash ~/.claude/skills/search-lit/references/pubmed_eutils.sh search \
  '"[Full Name]"[Author] AND "keyword2"' 5
# ... repeat for each suspected pillar keyword

Step 4 — MeSH term extraction for automatic pillar clustering:

# Fetch full XML for top-cited papers to extract MeSH headings
bash ~/.claude/skills/search-lit/references/pubmed_eutils.sh fetch \
  "PMID1,PMID2,...,PMID20" \
  | python3 -c "
import sys, xml.etree.ElementTree as ET
from collections import Counter
root = ET.fromstring(sys.stdin.read())
mesh_counts = Counter()
for article in root.findall('.//PubmedArticle'):
    for mh in article.findall('.//MeshHeading/DescriptorName'):
        mesh_counts[mh.text] += 1
for term, count in mesh_counts.most_common(30):
    print(f'{count:3d}  {term}')
"

→ Top MeSH terms reveal natural research pillars (e.g., "Colonography, Computed Tomographic" = CTC pillar).

Step 5 — Google Scholar profile (parallel with PubMed calls):

• WebSearch: "[Full Name]" radiology scholar google for h-index, citation data

Output: Pillar Summary Table

| Pillar | 영역 | 대표 키워드 | MeSH terms | 추정 논문 수 | |--------|------|------------|-----------|-------------| | 1 | ... | ... | ... | ~N+ |

Phase 2: MA Gap Analysis (Multi-Source)

Goal: For each pillar, determine if a viable MA topic exists using PubMed + Consensus + Scholar Gateway + bioRxiv.

For each pillar (run in parallel using meta-analyst agents):

2a. PubMed E-utilities — Existing MAs + Primary studies

# Existing MAs (structured count)
bash ~/.claude/skills/search-lit/references/pubmed_eutils.sh search \
  '[pillar keywords] AND ("meta-analysis"[pt] OR "systematic review"[pt])' 50

# Primary studies with extractable outcomes
bash ~/.claude/skills/search-lit/references/pubmed_eutils.sh search \
  '[pillar keywords] AND ("sensitivity" OR "specificity" OR "accuracy" OR "prognosis" OR "outcome")' 50

2b. Consensus MCP — Semantic MA gap detection

Use mcp__claude_ai_Consensus__search to find existing SRs/MAs that PubMed keyword search might miss:

query: "systematic review OR meta-analysis [pillar topic] [imaging modality]"

Consensus returns citation-ranked results — check if any highly-cited MA already covers the proposed scope. Limit: max 3 Consensus calls per Phase 2 batch (rate limit).

2c. Scholar Gateway — Semantic similarity search

Use mcp__claude_ai_Scholar_Gateway__semanticSearch for:

• Finding MAs with different terminology (e.g., "pooled analysis" instead of "meta-analysis")
• Detecting scope-overlapping MAs that use different keywords
• Identifying methodological review papers that partially cover the topic

2d. bioRxiv/medRxiv — In-press competition detection

Use mcp__claude_ai_bioRxiv__search_preprints to catch:

• MAs posted as preprints but not yet indexed in PubMed
• Ongoing SR/MA protocols shared as preprints
• Very recent primary studies that could change feasibility

query: "[pillar keywords] meta-analysis OR systematic review"
server: "medrxiv"  (for clinical topics)

2e. Assessment matrix

| Factor | Criteria | |--------|----------| | MA gap | 0 existing = best, 1-3 = check scope overlap, >5 = saturated | | Primary k | ≥8 for DTA, ≥6 for prognostic (minimum), ≥15 ideal | | Recency | Last MA >5 years old = updat