git:20260418.11caf95 to git:20260419.25f333d

---

name: ma-scout

description: Meta-analysis topic discovery and feasibility assessment. Professor-first (profile → gap) or Topic-first (question → gap → co-author). Pre-protocol phase from idea to ranked topic list.

triggers: ma-scout, MA 주제 찾기, professor MA, 메타분석 주제, MA gap, topic-first MA, 트렌드 MA, meta-analysis topic, 교수님 분석, 연구 분석

tools: Read, Write, Edit, Bash, Grep, Glob

model: opus

---

# 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 |

|--------|------|

## Communication Rules

- Research questions, PICO/PIRD, and README content in English.

- Medical terminology always in English.

---

## Inputs

### Mode A: Professor-first

- Profile URL (ScholarWorks, SKKU Faculty, Google Scholar, ORCID)

- PubMed author link (preferably with cauthor_id for disambiguation)

- Affiliation history (e.g., "Hospital A → Hospital B → retired")

- Minimum required: **name + at least one profile URL or PubMed link**

### Mode B: Topic-first

- MA type preference (DTA, prognostic, intervention — optional)

- 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.

---

# ═══════════════════════════════════════════

# MODE A: PROFESSOR-FIRST WORKFLOW

# ═══════════════════════════════════════════

### 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:**

- If cauthor_id is provided → fetch that specific PMID page to get full name + affiliation

- NEVER start with initials-only search (e.g., "Ha HK") — common Korean initials cause massive contamination

- First search must be `"[Full Name]"[Author]` (e.g., `"Ha Hyun Kwon"[Author]`)

2. **Confirm affiliation chain with user:**

- This prevents wrong-institution assumptions

- Skip only if user already provided explicit affiliation history

3. **Profile URL fallback chain** (Scopus requires auth, so plan alternatives):

- 1st: PubMed full name search (always works)

- 2nd: Google Scholar profile (WebSearch `"[Full Name]" radiology scholar`)

- 3rd: ResearchGate profile (WebSearch `"[Full Name]" researchgate radiology`)

- 4th: ScholarWorks / SKKU / university faculty page (if URL provided)

- 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

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):**

```bash

# 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):**

```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:**

```bash

# 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

```bash

# 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 = update opportunity |

| Competition | Check 2024-2026 for very recent MAs that block entry |

#### 2f. PROSPERO competition check (MANDATORY)

- Search PROSPERO via WebSearch: `site:crd.york.ac.uk/prospero [topic keywords]`

- Also try WebFetch: `https://www.crd.york.ac.uk/prospero/#searchadvanced`

- Look for registered-but-unpublished protocols that could block entry

- If PROSPERO match found → flag as 🚫 competition risk in ranking

#### 2g. Realistic k estimation

- Raw PubMed hit count is NOT the real k — most studies lack 2x2 data or HR

- Apply conservative discount: **k_realistic ≈ raw_count × 0.15–0.30** for DTA topics

- Flag if k_realistic < 8 (DTA) or < 6 (prognostic) as ⚠️ feasibility risk

#### 2h. Niche subtopic discovery (if pillar appears saturated)

- AI/radiomics angle on a classical topic

- Specific modality comparison (e.g., CEUS vs MRI)

- Treatment response (vs diagnosis which is often saturated)

- Specific subpopulation or disease subtype

- Use Consensus to check if the niche angle has already been covered

---

### Phase 3: Topic Ranking

**Goal:** Rank all viable topics by composite score.

Score each candidate on 5 criteria (★1-5):

| Criteria | Weight | Description |

|----------|--------|-------------|

**Output: Ranked Topic Table**

---

### Phase 4: Folder & README Scaffolding

**Goal:** Create project folders and README for each viable topic.

1. **Folder location:** `{working_dir}/ma-scout/{initials}_{professor_name}/`

- Professor folder: `{initials}_{name}` (e.g., `KDK_Kim`, `LKS_Lee`)

- NN: sequential number within professor (01, 02, ...)

- Check existing folders with `ls` before creating

3. **README.md template (PROSPERO-ready):**

---

### Phase 5: Output Summary

**Goal:** Persist findings for the user.

1. Save the ranked topic table and README files to the working directory.

2. Summarize: total topics scanned, viable topics found, recommended next steps.

3. Suggest the user save results to their project management system (e.g., `/manage-project`).

---

## Niche Topic Discovery Heuristics

2. **AI/radiomics overlay:** Classical imaging topic + AI approach = new MA angle

3. **Treatment response:** Diagnosis MAs saturated → treatment monitoring MA often open

4. **Modality comparison:** Head-to-head (e.g., CEUS vs MRI) often underserved

5. **Guideline gap:** Professor authored guidelines → MA supporting/updating those guidelines

7. **Temporal update:** Last MA >5 years old + significant new primary studies since

---

## Quality Gates

Before finalizing a topic as viable:

- [ ] **Author identity confirmed** — full name resolved via E-utilities efetch, no initials-only contamination

- [ ] **Affiliation confirmed** with user (or from reliable source)

- [ ] Confirmed MA = 0 or last MA >5 years (via PubMed E-utilities, not assumption)

- [ ] **Cross-validated via Consensus/Scholar Gateway** — no hidden MAs with different terminology

- [ ] **bioRxiv/medRxiv checked** — no preprint MA in progress

- [ ] Confirmed k_realistic ≥ 8 (DTA) or ≥ 6 (prognostic) after discount

- [ ] **PROSPERO searched** — no registered competing protocol found

- [ ] No 2024-2026 competing MA in press (check PubMed + preprints)

- [ ] Professor's publication record demonstrates clear authority in this area

- [ ] Research question is specific enough for PROSPERO registration

- [ ] **README contains:** complete PICO/PIRD, PubMed search strategy, Embase draft, target journal with IF, timeline

---

## Handoff

After MA Scout completes:

- To **`/meta-analysis`**: When a topic is approved and ready for PROSPERO protocol (README has PICO + search strategy ready)

- To **`manage-project`**: When project folder needs full scaffolding

- To **`search-lit`**: When deeper preliminary search is needed before committing

- To **`/analyze-stats`**: When feasibility requires power/sample-size calculation for the estimated k

---

## Parallel Execution Strategy

For efficiency, launch multiple agents and API calls in parallel:

**Phase 0 (Identity):**

1. E-utilities esearch: `"[Full Name]"[Author]` → total count + PMIDs (FIRST)

2. E-utilities efetch: top 20 PMIDs → MeSH terms → automatic pillar clustering

**Phase 1 (Profile — all parallel):**

3. Bash × 4-5: E-utilities esearch with topic-specific filters (parallel Bash calls)

4. WebSearch: Google Scholar profile

5. WebFetch: any provided profile URLs (skip Scopus)

**Phase 2 (MA Gap — multi-source parallel):**

6. Up to 4 meta-analyst agents in parallel, each covering 1-2 pillars

7. Each agent runs ALL of:

- E-utilities esearch: existing MA count + primary study count

- Consensus MCP: semantic MA search (max 3 calls total across all agents)

- Scholar Gateway: scope-overlap check

- bioRxiv/medRxiv: preprint MA detection

- PROSPERO: competition check (WebSearch)

8. Each agent reports: raw k, realistic k (15-30% discount), all sources checked

**Phase 3 (Ranking):** Sequential, uses Phase 2 outputs.

**Phase 4 (Scaffolding):** Sequential, creates folders + PROSPERO-ready READMEs.

Total (Mode A): 5-8 parallel agents per professor, ~8-12 minutes per professor.

### Mode B Parallel Strategy

**T-Phase 0:** Sequential (user interaction for scope clarification).

**T-Phase 1 (Landscape — all angles in parallel):**

1. Per angle: Bash (PubMed MA count) + Bash (primary k) + Consensus + bioRxiv + PROSPERO

2. 3-5 angles × 5 sources = 15-25 parallel calls

**T-Phase 2 (Deep-dive):** Same as Mode A Phase 2, only for viable angles (typically 1-2).

**T-Phase 4 (Co-author — if needed):**

3. Bash: PubMed author frequency search

4. WebSearch: Google Scholar profiles for top candidates

Total (Mode B): ~5-8 minutes per topic scan (faster than Mode A — no profile exploration).

### Known Pitfalls (from 3 professor analyses)

- Common Korean/Asian initials (e.g., "Lee KS", "Kim DK") return 300+ papers with massive contamination. Always use full name first.

- Scopus/ScienceDirect → 403 or redirect to login. Never rely on Scopus as primary data source.

- Raw PubMed counts overestimate by 3-7x. ~130 hits often means 20-40 with extractable DTA data.

- Professor may have moved institutions. Don't assume affiliation without verification.

- **Consensus rate limit:** Max 3 batch calls. If rate-limited, wait 30s and retry once.

- **E-utilities rate limit:** 350ms between calls (100ms with NCBI_API_KEY). Scripts handle this automatically.

- **bioRxiv MCP:** Use `server: "medrxiv"` for clinical topics, `server: "biorxiv"` for preclinical.

---

# ═══════════════════════════════════════════

# MODE B: TOPIC-FIRST WORKFLOW

# ═══════════════════════════════════════════

### T-Phase 0: Topic Clarification & Scope

**Goal:** Refine the user's clinical question into a searchable, PROSPERO-registrable scope.

1. **Parse the input** — extract:

- Disease/condition (e.g., "lung nodule", "hepatocellular carcinoma")

- Imaging modality or intervention (e.g., "dual-energy CT", "AI CAD")

- Outcome type: DTA (Se/Sp), prognostic (HR/OR), intervention (RR/MD), dosimetry

- Population specifics (e.g., "screening setting", "cirrhotic patients")

2. **Expand to neighboring angles** — propose 3-5 variations:

```

```

3. **User selects 1-3 angles** to investigate further.

---

### T-Phase 1: Landscape Scan (Multi-Source)

**Goal:** For each selected angle, rapidly assess the MA landscape.

**Run all angles in parallel. For each angle:**

#### 1a. PubMed — Existing MA count

```bash

bash ~/.claude/skills/search-lit/references/pubmed_eutils.sh search \

'[topic keywords] AND ("meta-analysis"[pt] OR "systematic review"[pt])' 50

```

#### 1b. PubMed — Primary study pool

```bash

bash ~/.claude/skills/search-lit/references/pubmed_eutils.sh search \

'[topic keywords] AND ("sensitivity" OR "specificity" OR "hazard" OR "outcome")' 100

```

#### 1c. Consensus MCP — Semantic MA discovery

```

query: "systematic review [topic] [modality]"

```

Check for MAs using different terminology.

#### 1d. bioRxiv/medRxiv — Preprint competition

```

query: "[topic] meta-analysis"

server: "medrxiv"

```

#### 1e. PROSPERO — Registered protocols

WebSearch: `site:crd.york.ac.uk/prospero [topic keywords]`

**Output: Landscape Summary Table**

| 변형 | 기존 MA | Primary k (raw) | k (realistic) | PROSPERO | Preprint MA | 판정 |

|------|---------|----------------|--------------|----------|------------|------|

| 1 | 3편 | 120 | 18-36 | 1건 | 0 | ⚠️ 경쟁 |

| 2 | 0편 | 85 | 13-25 | 0 | 0 | ✅ 최적 |

---

### T-Phase 2: Feasibility Deep-Dive

**Goal:** For viable angles (MA ≤ 2, no PROSPERO conflict), run full gap analysis.

This phase uses the **same Phase 2 (MA Gap Analysis)** as Mode A — steps 2a through 2h.

The only difference: no "Professor fit" to evaluate, so focus on:

- **Gap certainty** — are existing MAs truly non-overlapping with proposed scope?

- **k quality** — are primary studies heterogeneous enough to warrant MA, or too uniform?

- **User's domain fit** — does this align with user's radiology AI / imaging expertise?

---

### T-Phase 3: Topic Ranking (Topic-first weights)

**Goal:** Rank viable topics with weights adjusted for topic-first approach.

| Criteria | Weight | Description |

|----------|--------|-------------|

| **MA gap** | 최고 | 기존 MA 없음 > update 기회 > 포화 |

| **Feasibility (k)** | 최고 | k_realistic ≥ 8 (DTA) or ≥ 6 (prognostic) |

| **User domain fit** | 높음 | 사용자의 전문 분야와 맞는가 |

| **Clinical impact** | 중간 | 가이드라인 변경 가능성, 임상 의사결정 직결 |

| **Co-author availability** | 중간 | 해당 분야 전문가 접근 가능성 (기존 관계 or 접근 용이) |

| **Execution ease** | 중간 | 단독 진행 가능 vs 전문가 해석 필수 |

**Output: Ranked Topic Table**

| 순위 | 주제 | 기존 MA | 추정 k | PROSPERO | Co-author 필요 | 종합 |

|------|------|---------|--------|----------|--------------|------|

| 1 | ... | 0편 | 25 | 없음 | 선택적 | ✅ 최적 |

---

### T-Phase 4: Co-Author Matching (Optional)

**Goal:** If the user wants a senior co-author, find candidates.

**Strategy 1 — Existing network (memory-based):**

- Check memory files for professors with overlapping expertise

- Cross-reference existing professor folders in the working directory

- Best match = professor whose pillar naturally covers this topic

**Strategy 2 — PubMed reverse search:**

```bash

# Find prolific authors in this specific topic

bash ~/.claude/skills/search-lit/references/pubmed_eutils.sh search \

'[topic keywords] AND ("{user_country}"[Affiliation])' 100

```

Then:

- E-utilities efetch → extract author frequency from results

- Top 5 most-published authors in this niche = potential co-authors

- Cross-check Google Scholar for h-index and recent activity

**Strategy 3 — Self-led (no senior co-author):**

- Viable when: user has 2+ published MAs, topic is methodologically straightforward

- Still need 2nd reviewer (junior colleague or peer) — flag this in README

- Corresponding author = user

---

### T-Phase 5: Folder & README Scaffolding (Topic-first)

**Goal:** Create project folder and PROSPERO-ready README.

1. **Folder location:** `{working_dir}/ma-scout/TOPIC/`

- Topic-first projects use `TOPIC/` prefix (not professor initials)

- Naming: `{NN}_{Topic_Abbreviation}/` (e.g., `01_AI_Lung_Nodule_DTA/`)

- If co-author matched later, can be moved under professor folder

- `Supervisor:` → `Lead: {user_name}` or `Lead: {user_name} + {co-author}`

**Timeline template (self-led):**

3. **Summary:** Same as Mode A Phase 5 — save ranked results and recommend next steps.

---

### Topic Discovery Heuristics (Mode B specific)

1. **Trend scan** — Search recent high-IF radiology journals for "gap in the literature" + "meta-analysis needed":

```bash

bash ~/.claude/skills/search-lit/references/pubmed_eutils.sh search \

'"no meta-analysis" AND "radiology"[Journal] AND 2024:2026[dp]' 30

```

2. **Guideline update gaps** — New guidelines (ACR, ESR, RSNA) often cite lack of MA evidence:

- Consensus search: `"practice guideline" AND "insufficient evidence" AND [radiology subspecialty]`

3. **AI + classical imaging** — Overlay AI/DL/radiomics on well-studied classical topics:

- Many classical DTA topics have 10+ MAs, but AI angle has 0-1

4. **Korean/Asian population** — Population-specific MA for diseases with geographic variation:

- TB, NTM, parasitic diseases, gastric cancer, liver fluke, HBV-related HCC

5. **Technology adoption** — New modalities with growing evidence but no synthesis:

- Photon-counting CT, abbreviated MRI, contrast-enhanced mammography, AI CAD

6. **Cross-subspecialty** — Topics spanning two subspecialties often fall through MA cracks:

- Cardiac + thoracic (coronary CT + lung screening), neuro + MSK (spine imaging)

---

### Quality Gates (Mode B specific)

Before finalizing a topic-first MA as viable:

- [ ] Clinical question refined to PICO/PIRD (not just a keyword)

- [ ] MA gap confirmed via PubMed + Consensus + Scholar Gateway + bioRxiv (all 4 sources)

- [ ] k_realistic ≥ 8 (DTA) or ≥ 6 (prognostic) after 15-30% discount

- [ ] PROSPERO searched — no competing registered protocol

- [ ] No 2024-2026 competing MA in press or preprint

- [ ] User's domain expertise sufficient for clinical interpretation (or co-author identified)

- [ ] 2nd reviewer identified or plan to recruit

- [ ] README contains: complete PICO/PIRD, PubMed + Embase search strategy, target journal with IF, timeline

- [ ] If self-led: user has ≥ 2 published MAs (otherwise, recommend co-author)

---

## Phase 6: Pre-Proposal Pipeline (Post-Scout)

After MA Scout identifies viable topics, run the **pre-proposal pipeline** to prepare

a "ready-to-propose" package before contacting the professor.

### Pipeline Steps

1. **Search Execution** — E-utilities with broadened synonyms (retmax=200)

- Primary search: `[topic] AND [outcome keywords]`

- Existing MA search: `[topic] AND ("meta-analysis"[pt] OR "systematic review"[pt])`

2. **Metadata Collection** — `fetch_json` → `esummary` (batch 40-50 PMIDs)

3. **Title-Based Triage** — Classify as INCLUDE / MAYBE / EXCLUDE

- CRITICAL: Check for existing MAs within results (initial scout may miss them)

- Separate bronchoscopic vs percutaneous (30% contamination in CBCT topics)

- Flag professor's own papers (authority evidence)

- Flag retracted papers

4. **PRISMA Flow Draft** — Identification → Screening → Eligibility → Included (estimated)

5. **Gap Re-assessment** — Update MA count, re-position if needed:

- MA=0 → "first MA" | MA=1 (>5yr) → "update MA" | MA≥3 (recent) → skip/niche

6. **Output Files**:

- `candidates.md` — full triage table + PRISMA flow + gap finding

- `README.md` — updated Preliminary Search section with actual numbers

### Parallel Execution

- Launch up to 4 agents per wave (each topic independent)

- Each agent: search → fetch → triage → write files → ~5-10 min

- 21 topics completed in ~1 hour with 16 parallel agents

### Professor Contact Package

The pre-proposal gives the professor:

## Anti-Hallucination

- **Never fabricate publication counts, h-index, or pillar classifications.** All numbers must come from PubMed E-utilities API output.

- **Never fabricate existing MA counts.** Always verify via PubMed search + PROSPERO check before claiming "MA = 0".

- **Never invent professor expertise or affiliation.** Confirm with user before proceeding.

- **k_realistic must use the 15-30% discount.** Raw PubMed counts overestimate by 3-7x. Always report both raw and realistic estimates.

- If PubMed returns 0 or Consensus/Scholar Gateway is unavailable, state the limitation rather than guessing.