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Skillsma-scout

ma-scout

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Updated 10-15 16:51

About this skill

MA Scout Skill

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

Mode Selection

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).

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; 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; radiology subspecialty scope; 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.

Query PubMed with /search-lit's E-utilities scripts, not WebFetch — they are faster and return structured JSON/XML: ${CLAUDE_SKILL_DIR}/../search-lit/references/pubmed_eutils.sh and parse_pubmed.py beside it. Rate limit: 350 ms between calls (100 ms with NCBI_API_KEY).

MODE A: PROFESSOR-FIRST WORKFLOW

Phase 0: Disambiguation & Context Confirmation

Resolve the author's identity BEFORE any PubMed search:

  1. Resolve the full English name first. If a cauthor_id is provided, fetch that PMID page to get the full name + affiliation. NEVER start with an initials-only search, because common Korean/Asian initials (e.g., "Lee KS") return 300+ papers with massive contamination. The first search must be "[Full Name]"[Author].
  2. Confirm the affiliation chain with the user. Ask whether {detected affiliation} matches the professor's history (professors move institutions — do not assume), and ask the user's relationship to the professor so topic proposals can be tuned. Skip only if the user already gave an explicit affiliation history.
  3. Profile URL fallback chain:
    • 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 — returns 403 or a login redirect; never rely on it

Phase 1: Profile Exploration (E-utilities API)

Goal: Identify the professor's 5-6 distinct research pillars.

Step 1 — Total publication count + PMID list:

bash ${CLAUDE_SKILL_DIR}/../search-lit/references/pubmed_eutils.sh search \
  '"[Full Name]"[Author]' 200 \
  | python3 ${CLAUDE_SKILL_DIR}/../search-lit/references/parse_pubmed.py esearch

If the parser exits non-zero (an error body, or no count), the count is unknown — re-run the search; never record it as 0 papers or 0 MAs.

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

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

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

bash ${CLAUDE_SKILL_DIR}/../search-lit/references/pubmed_eutils.sh search \
  '"[Full Name]"[Author] AND "keyword1"' 5
bash ${CLAUDE_SKILL_DIR}/../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_SKILL_DIR}/../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 and citation data; WebFetch any other profile URL the user provided (skip Scopus).

Output: Pillar Summary Table. Publication counts and pillar assignments come from E-utilities output and the h-index from the Scholar profile — never estimate them.

Pillar Domain Representative keywords MeSH terms Est. # papers
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 + PROSPERO.

Run pillars in parallel: up to 4 subagents, each covering 1-2 pillars and running 2a–2g, each reporting raw k, realistic k and every source checked. If PubMed returns 0 or Consensus/Scholar Gateway is unavailable, state that limitation rather than guessing.

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

# Existing MAs (structured count)
bash ${CLAUDE_SKILL_DIR}/../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_SKILL_DIR}/../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, in total across all agents (rate limit). If rate-limited, wait 30 s and retry once.

2c. Scholar Gateway — Semantic similarity search

Use mcp__claude_ai_Scholar_Gateway__semanticSearch to find MAs under different terminology (e.g., "pooled analysis" instead of "meta-analysis"), scope-overlapping MAs that use different keywords, and methodological reviews 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 in PubMed, SR/MA protocols shared as preprints, and very recent primary studies that could change feasibility.

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

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 the last two years 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 a PROSPERO match is found → flag as 🚫 competition risk in ranking
  • Record the exact query, the search date and one status: searched (match found), searched (no match), or not checked (unavailable/failed); a fetch that returns the site shell or an error page instead of a result list is not checked. Only searched (no match) clears the PROSPERO gate.

2g. Realistic k estimation

  • Raw PubMed hit count is NOT the real k — most studies lack 2x2 data or HR, so raw counts overestimate by 3-7x
  • 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
  • Report both raw and realistic estimates, e.g., estimated k: ~130 (raw) → ~20–40 (extractable DTA data)

2h. Niche subtopic discovery (if pillar appears saturated, >5 prior MAs)

Try these angles, and use Consensus to check whether the niche angle has already been covered:

  1. "First MA" rule: the professor's most unique/niche subtopic where MA = 0
  2. AI/radiomics overlay: classical imaging topic + AI approach
  3. Treatment response: diagnosis MAs are often saturated; treatment monitoring is often open
  4. Modality comparison: head-to-head (e.g., CEUS vs MRI) is often underserved
  5. Guideline gap: professor-authored guidelines → MA supporting/updating them
  6. Population niche: specific subpopulation, disease subtype, or regional population (e.g., parasitic diseases, TB)
  7. Temporal update: last MA >5 years old + significant new primary studies since

Phase 3: Topic Ranking

Goal: Rank all viable topics by composite score. Score each candidate on 5 criteria (★1-5):

Criteria Weight Description
Professor fit Highest Core area of the professor's career, publication count, distinctive contribution
MA gap High No prior MA > ≥5 yr since last MA > recent MA exists
Feasibility (k) High Number of includable studies and extractability of 2×2 or HR data
Clinical impact Medium Whether the topic directly informs clinical decision-making
Execution ease Medium Completable from literature alone; difficulty of managing heterogeneity

Output: Ranked Topic Table

Rank Topic Professor's Pillar Prior MA Estimated k (raw→realistic) PROSPERO competition Verdict
1 ... ... 0 ~98 → 15–30 None ✅ Best fit

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}/{NN}_{topic_slug}/
    • Professor folder: {initials}_{name} (e.g., KDK_Kim, LKS_Lee)
    • NN: sequential number within professor (01, 02, ...); topic_slug: English, underscore-separated
    • Check existing folders with ls before creating
  2. README.md (PROSPERO-ready): copy the template block from ${CLAUDE_SKILL_DIR}/references/project_readme_template.md into {topic_folder}/README.md and fill it (PICO/PIRD frame, preliminary search, target journal table, backward-planned timeline). Write the research question, PICO/PIRD and README content in English, medical terms always in English, unless the user asks for the Korean PI-facing variant that reference names.

Phase 5: Output Summary

  1. Save the ranked topic table and README files to the working directory.
  2. Summarize: total topics scanned, viable topics found, recommended next steps (see Handoff).
MODE B: TOPIC-FIRST WORKFLOW

T-Phase 0: Topic Clarification & Scope

Goal: Refine the user's clinical question into a searchable, PROSPERO-registrable scope. When the user asks for topic suggestions without a specific idea, read ${CLAUDE_SKILL_DIR}/references/topic_discovery_heuristics.md first to generate candidate questions.

  1. Parse the input — disease/condition (e.g., "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, e.g.: 
    user input: "AI for lung nodule malignancy prediction"
→ variant 1: AI vs radiologist for lung nodule malignancy prediction (DTA)
→ variant 2: Radiomics for lung nodule malignancy (DTA)
→ variant 3: Deep learning for incidental pulmonary nodule management (prognostic)
  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 ${CLAUDE_SKILL_DIR}/../search-lit/references/pubmed_eutils.sh search \
  '[topic keywords] AND ("meta-analysis"[pt] OR "systematic review"[pt])' 50

1b. PubMed — Primary study pool

bash ${CLAUDE_SKILL_DIR}/../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

Variant Existing MAs Primary k (raw) k (realistic) PROSPERO Preprint MA Verdict
1 3 120 18-36 1 0 ⚠️ Competitive
2 0 85 13-25 0 0 ✅ Optimal

T-Phase 2: Feasibility Deep-Dive

Goal: For viable angles (MA ≤ 2, no PROSPERO conflict), run the same Phase 2 (MA Gap Analysis) as Mode A — steps 2a through 2h. With no "Professor fit" to evaluate, 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)

Criteria Weight Description
MA gap Highest No existing MA > update opportunity > saturated
Feasibility (k) Highest k_realistic ≥ 8 (DTA) or ≥ 6 (prognostic)
User domain fit High Does it match the user's area of expertise?
Clinical impact Medium Potential to change guidelines; directly tied to clinical decisions
Co-author availability Medium Access to a domain expert (existing relationship or easy to reach)
Execution ease Medium Can be done solo vs requires expert interpretation

Output: Ranked Topic Table

Rank Topic Existing MAs Est. k PROSPERO Co-author needed Overall
1 ... 0 25 None Optional ✅ Optimal

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

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

Strategy 1 — Existing network: check memory files and existing professor folders in the working directory for professors whose pillar naturally covers this topic (best match).

Strategy 2 — PubMed reverse search:

# Find prolific authors in this specific topic
bash ${CLAUDE_SKILL_DIR}/../search-lit/references/pubmed_eutils.sh search \
  '[topic keywords] AND ("{user_country}"[Affiliation])' 100

Then E-utilities efetch → author frequency; the top 5 most-published authors in this niche are potential co-authors. Cross-check Google Scholar for h-index and recent activity.

Strategy 3 — Self-led (no senior co-author): viable when the user has 2+ published MAs and the topic is methodologically straightforward. A 2nd reviewer (junior colleague or peer) is still needed — flag this in the README. Corresponding author = user.

Output: Co-author recommendation table or a "solo-viable" judgment.

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

  1. Folder location: {working_dir}/ma-scout/TOPIC/{NN}_{Topic_Abbreviation}/ (e.g., 01_AI_Lung_Nodule_DTA/). Topic-first projects use the TOPIC/ prefix, not professor initials; if a co-author is matched later, the folder can move under the professor folder.
  2. README.md: the Phase 4 template with the Solo-Mode Adaptations in ${CLAUDE_SKILL_DIR}/references/project_readme_template.md (Lead/Domain rows, Team Expertise, self-led timeline).
  3. Summary: same as Mode A Phase 5 — save ranked results and recommend next steps.

Quality Gates

Before finalizing a topic as viable:

  • (A) Author identity confirmed — full name resolved via E-utilities efetch, no initials-only contamination
  • (A) Affiliation confirmed with user (or from reliable source)
  • (A) Professor's publication record demonstrates clear authority in this area
  • (B) Clinical question refined to PICO/PIRD (not just a keyword)
  • (A) Confirmed MA = 0 or last MA >5 years (via PubMed E-utilities, not assumption)
  • Cross-validated via PubMed plus every connected semantic or preprint source (Consensus, Scholar Gateway, bioRxiv/medRxiv); a source that was unavailable is named and recorded as not checked, never as "no competing work found" — no hidden MAs with different terminology, no preprint MA in progress
  • Confirmed k_realistic ≥ 8 (DTA) or ≥ 6 (prognostic), where k_realistic = raw count × 0.15–0.30 (§2g: keep 15–30% of raw hits, i.e. a 70–85% discount — not a 15–30% discount)
  • PROSPERO searched — the search ran and returned results (query + date recorded, §2f), and no registered competing protocol was found. A PROSPERO search that failed or was unavailable is "not checked", never "none found": this item stays open
  • No competing MA from the last two years in press or preprint
  • Research question is specific enough for PROSPERO registration
  • (B) User's domain expertise sufficient for clinical interpretation (or co-author identified); 2nd reviewer identified or plan to recruit
  • (B) If self-led: user has ≥ 2 published MAs (otherwise, recommend co-author)
  • README contains: complete PICO/PIRD, PubMed search strategy, Embase draft, target journal with IF, timeline

Handoff

  • To /meta-analysis: when a topic is approved and ready for the PROSPERO protocol (README has PICO + search strategy)
  • To /manage-project: when the project folder needs full scaffolding or the user wants results saved to project management
  • To /search-lit: when a deeper preliminary search is needed before committing
  • To /analyze-stats: when feasibility requires power/sample-size calculation for the estimated k

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

After MA Scout identifies viable topics, prepare a "ready-to-propose" package before contacting the professor. Topics are independent: run up to 4 agents per wave, each doing search → fetch → triage → write files.

  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
    • Check for existing MAs within the results — the initial scout may have missed them
    • Separate sub-approaches that contaminate the pool (e.g., bronchoscopic vs percutaneous)
    • Flag the professor's own papers (authority evidence) and retracted papers
  4. PRISMA Flow Draft — Identification → Screening → Eligibility → Included (estimated)
  5. Gap Re-assessment — update the MA count and 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

Professor Contact Package — the pre-proposal gives the professor: candidate count + gap evidence (e.g., "MA = 0, 35 studies to include"), a clear role description (e.g., "independent screening review + discussion only"), and the urgency of PROSPERO pre-registration to secure the topic.