brainstorm-topic

name	brainstorm-topic
description	Interactive brainstorming to develop a meta-analysis topic. Use when user wants to explore ideas, refine a topic, or needs help formulating a research question.

Topic Brainstorming Skill (Enhanced v2.0)

Guide users through developing a feasible, well-formed meta-analysis topic via structured interactive conversation.

⚠️ CRITICAL FOR AI AGENTS: This skill contains SELF-CHECK PROMPTS throughout. Follow them to avoid common pitfalls.

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Trigger Phrases

• "help me find a topic"
• "brainstorm topic"
• "I'm not sure what to study"
• "help me refine my topic"
• "/brainstorm"

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🎯 Core Principles (READ FIRST)

For AI Agents: Your Mindset

1. You are a GUARDIAN against wasted effort - Your job is to help user find a feasible question, not just any question
2. Flag red flags immediately - Don't wait until Phase 3 to warn about problems
3. Offer examples constantly - Users often don't know what's possible
4. Balance ambition with feasibility - Encourage interesting questions, but ground in reality
5. Document your reasoning - Explain WHY you suggest/reject certain directions

For Users: What Makes a Good Topic

✅ GOOD topics:

• Specific intervention (e.g., "SSRIs" not "antidepressants")
• Well-defined population (e.g., "adults with MDD" not "depressed people")
• Measurable outcomes (e.g., "response rate" not "feeling better")
• 5+ RCTs expected
• Clear comparator

❌ BAD topics (avoid these):

• Too broad ("cancer treatment")
• Too narrow ("CDK4/6 inhibitors in triple-negative breast cancer with PIK3CA mutations")
• No quantitative outcomes
• <3 studies likely
• Every study compares different things

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📋 Structured Conversation Flow

Phase 0: Pre-Assessment (NEW)

Before starting PICO, ask:

👋 Hi! Before we dive in, let me ask a few quick questions to guide us:

1. Have you done a meta-analysis before? (yes/no)
2. Do you have a specific topic in mind, or are you exploring? (specific/exploring)
3. What's your timeline? (urgent <2 weeks / standard 1-2 months / flexible)
4. Do you have institutional journal access? (yes/no)

🤖 AI SELF-CHECK: Based on answers, adjust your guidance style:

• First-timer? → Offer more examples, explain concepts
• Specific topic? → Jump to feasibility check first, then refine PICO
• Urgent? → Steer towards established topics with known studies
• No access? → Emphasize Open Access studies

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Phase 1: Clinical Area Exploration

Ask ONE question at a time. Start with:

What clinical area or health topic interests you?

Examples:

• 🧠 Mental health (depression, anxiety, PTSD)
• ❤️ Cardiovascular (heart failure, hypertension, stroke)
• 🎗️ Oncology (breast, lung, colorectal cancer)
• 🏥 Surgery (minimally invasive, robotic, outcomes)
• 💊 Pharmacotherapy (drug comparisons, adherence)
• 🏃 Rehabilitation (physical therapy, post-stroke, orthopedic)
• 🍎 Nutrition & lifestyle (diet, exercise, supplements)

🤖 AI SELF-CHECK after user answers:

• Is this area too broad? (e.g., "cancer" → needs narrowing)
• Is this area too narrow? (e.g., "stage IIIb NSCLC with EGFR exon 19 deletion" → likely <5 studies)
• Is this area well-researched? (mental health, cardio → yes; rare diseases → maybe not)

If red flag detected, immediately say:

⚠️ Quick heads-up: [Area] is quite [broad/narrow]. Let me help you narrow/broaden this...

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Phase 2: PICO Element-by-Element (WITH CHECKPOINTS)

2A. Population (P)

Within [their area], which patient group interests you most?

Consider:

• Age: Pediatric? Adult? Elderly?
• Disease stage: Early? Advanced? Any stage?
• Setting: Inpatient? Outpatient? Primary care?
• Comorbidities: Specific groups? (e.g., diabetes + heart disease)

🤖 AI SELF-CHECK after user answers:

• Is population too narrow? (e.g., "adults 65-70 with HbA1c 7.5-8.0" → very few studies)
• Is population unclear? (e.g., "sick people" → needs specificity)
• Will this population have enough studies? (use your knowledge: common conditions → yes; ultra-rare → no)

Instant Feasibility Check (NEW):

After getting P, run a quick mental/web check:

🔍 Mental check: "How many RCTs exist for [this population]?"
- Common conditions (diabetes, depression, hypertension): Thousands → ✅
- Moderately common (COPD, Parkinson's): Hundreds → ✅
- Rare diseases (Cushing's, NMO): Tens → ⚠️
- Ultra-rare (specific gene mutations): <10 → ❌

If ⚠️ or ❌, immediately flag:

⚠️ Just so you know, [population] is relatively rare. This might limit the number of available studies. Want to broaden slightly, or shall we continue and check later?

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2B. Intervention (I)

What treatment or intervention do you want to evaluate?

Common types:

• 💊 Drug vs drug (e.g., SSRI vs SNRI)
• 🧪 Drug vs placebo (e.g., new medication efficacy)
• 🧘 Therapy vs control (e.g., CBT vs waitlist)
• 🏥 Procedure A vs B (e.g., robotic vs open surgery)
• 🎯 Dose comparison (e.g., high-dose vs standard-dose)
• 📱 Delivery method (e.g., telehealth vs in-person)

🤖 AI SELF-CHECK after user answers:

• Is intervention too vague? (e.g., "therapy" → specify CBT, IPT, etc.)
• Is intervention too specific? (e.g., "drug X manufactured by company Y batch 2023" → unnecessarily narrow)
• Is this intervention established enough to have studies? (FDA-approved drugs → yes; experimental → maybe)

Instant Red Flag Check (NEW):

❌ STOP if:

• Intervention is a specific brand name and user wants only that brand (too narrow)
• Intervention is extremely new (FDA approved <1 year ago, unlikely to have RCTs)
• Intervention is obsolete (no longer used in practice → studies exist but not clinically relevant)

If detected, immediately warn:

🚨 Red flag: [Intervention] is [too new/too specific/obsolete]. This might severely limit available studies or clinical relevance. Let me suggest alternatives...

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2C. Comparator (C)

What should we compare it against?

Options:

• 🔵 Placebo/sham (cleanest effect estimate, but ethics may limit availability)
• 🟢 Active comparator (another drug/therapy - more clinically relevant)
• 🟡 Standard of care / Treatment as usual (real-world comparison)
• ⚪ Waitlist control (common in psychotherapy)
• 🔴 No treatment (rare, ethical concerns)

🤖 AI SELF-CHECK after user answers:

• Does this comparator make clinical sense? (e.g., placebo for life-threatening condition → unethical, unlikely to find studies)
• Will there be enough studies with THIS specific comparison? (common comparison → yes; unusual → maybe not)
• Is the comparison fair? (dose X vs dose Y should use same drug, not different drugs)

Heterogeneity Warning (NEW):

If you suspect studies will use different comparators, warn early:

⚠️ Heads-up: Studies on [intervention] often compare against different controls (some use placebo, some use active comparator). This might create heterogeneity. Want to focus on one specific comparison type, or include all?

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2D. Outcomes (O)

What outcomes matter most to you?

Think about:

• 🎯 Primary outcome (main thing you want to measure)
  • Mortality / survival
  • Symptom reduction (scales, scores)
  • Disease progression
  • Quality of life
  • Functional status
• 🔍 Secondary outcomes (nice to have)
  • Adverse events / safety
  • Adherence / dropout
  • Cost-effectiveness
  • Subgroup effects

🤖 AI SELF-CHECK after user answers:

• Is primary outcome quantifiable? (e.g., "depression score" → yes; "feeling better" → too vague)
• Is outcome commonly reported? (survival, response rate → yes; rare biomarkers → maybe not)
• Can outcome be pooled across studies? (same measurement tool? or different scales?)

CRITICAL Feasibility Check (NEW):

Ask yourself: "Do studies on [intervention] typically report [outcome]?"

Examples:

• ✅ Drug trials → almost always report adverse events
• ✅ Depression trials → almost always report symptom scales (PHQ, BDI, HAM-D)
• ❌ Old surgical studies → may NOT report QoL (only mortality)
• ❌ Pilot studies → may report feasibility but not clinical outcomes

If outcome might not be reported, warn:

⚠️ Quick reality check: Many studies on [intervention] may not report [outcome]. Let me do a quick search to verify...

Then run WebSearch:

WebSearch: "[intervention] [condition] [outcome] randomized trial"

Check if outcome appears in abstracts. If <30% mention it, flag immediately:

🚨 Concern: My quick search shows only ~X% of studies report [outcome]. You might end up with very few includable studies. Want to add a more commonly reported outcome as alternative?

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Phase 3: Early Feasibility Assessment (ENHANCED)

⚠️ DO THIS IMMEDIATELY AFTER PICO IS COMPLETE, BEFORE FINALIZING

This is NOT optional. Run all checks below:

3A. Existing Systematic Reviews

Search #1: Recent reviews

WebSearch: "[intervention] [condition] systematic review meta-analysis 2024 OR 2025 OR 2026"

🤖 AI SELF-CHECK:

• Found recent systematic review (within 2 years)?
  • ✅ Yes, and outdated: Good! You can update it
  • ⚠️ Yes, and recent (<1 year): Uh-oh, may be redundant. Check if you can add new angle (subgroup, new outcome)
  • ✅ No recent review: Great! Clear need for new synthesis

If recent review exists, present to user:

📚 I found a systematic review published [date] titled "[title]".

Options:

1. Update this review (add new studies published since)
2. Focus on a subgroup they didn't analyze (e.g., only elderly patients)
3. Add new outcome they didn't include
4. Different comparison they didn't examine

Which appeals to you?

Search #2: Cochrane Library (gold standard)

WebSearch: "cochrane review [intervention] [condition]"

• Cochrane review exists and recent (<3 years)? → Probably don't compete, find different angle
• Cochrane review outdated (>3 years)? → Updating is valuable
• No Cochrane review? → Green light!

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3B. Study Volume Estimation

Search #3: RCT count

WebSearch: "[intervention] [population] [outcome] randomized controlled trial"

🤖 AI SELF-CHECK:

• How many results mentioned in search snippet?
  • ✅ 10+ RCTs mentioned: Excellent, proceed
  • ⚠️ 5-9 RCTs: Marginal, doable but small
  • ❌ <5 RCTs: Too few, STOP or revise PICO

Alternative: PubMed Clinical Queries (more accurate)

If you have access, suggest user run:

PubMed Clinical Queries:
([intervention] AND [condition] AND [outcome]) AND (randomized controlled trial[pt])
Filter: Therapy/Narrow

Report findings:

🔍 Feasibility snapshot:

• Estimated RCTs: ~[X] studies
• Most recent: [year]
• Assessment: ✅ Sufficient / ⚠️ Marginal / ❌ Too few

[If marginal/too few]: Want to broaden the PICO to capture more studies?

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3C. Heterogeneity Risk Assessment (NEW)

🤖 AI MENTAL CHECK: Based on PICO, assess heterogeneity risk:

Low risk ✅ (proceed confidently):

• Same drug class, same dose range
• Same population (e.g., all adults with MDD)
• Same outcome measurement (e.g., all use HAM-D scale)

Moderate risk ⚠️ (flag to user):

• Different drugs within same class (e.g., various SSRIs)
• Mixed populations (e.g., adults + elderly)
• Different scales measuring same construct (e.g., BDI vs HAM-D vs PHQ-9)

High risk ❌ (warn strongly):

• Different interventions entirely (e.g., drug + therapy mixed)
• Vastly different populations (e.g., pediatric + adult + elderly)
• Incompatible outcomes (e.g., some report mortality, some report QoL)

If moderate/high risk, warn:

⚠️ Heterogeneity concern: Based on your PICO, studies might compare different [interventions/populations/outcomes], making pooling difficult. You may need subgroup analysis or sensitivity analysis to handle this. Aware of this complexity, or want to narrow PICO?

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3D. Data Availability Check (NEW)

🤖 AI MENTAL CHECK: Will studies report extractable data?

Good scenarios ✅:

• Mortality (always binary: dead/alive)
• Response rate (always binary: responded/not)
• Continuous outcomes with standard scales (HAM-D, MMSE, etc.)

Risky scenarios ⚠️:

• Rare outcomes (event rate <5% → need huge sample sizes)
• Composite outcomes (definitions vary across studies)
• Proprietary scales (not widely used, hard to compare)

Bad scenarios ❌:

• Qualitative outcomes only
• "Improvement" without definition
• Outcomes reported as medians (can't pool easily)

If risky/bad, ask user:

🤔 Quick question: Are you comfortable with the possibility that some studies might not report [outcome] in a poolable format? You might need to contact authors for raw data, or exclude some studies. Okay with that?

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Phase 4: Refined Topic Presentation (WITH FEASIBILITY REPORT)

After all checks, present structured topic + feasibility summary:

## 🎯 Your Meta-Analysis Topic

**Research Question:**
[Full PICO question in sentence form]

**Population:** [specific group]
**Intervention:** [specific treatment]
**Comparator:** [specific control]
**Outcomes:**
- Primary: [main outcome]
- Secondary: [additional outcomes]

**Study Designs:** RCTs [+ observational if justified]

---

## ✅ Feasibility Assessment (Quick Check)

**Study Volume**: ~[X] RCTs estimated
**Recent Reviews**: [None / Update available / Recent exists]
**Heterogeneity Risk**: ✅ Low / ⚠️ Moderate / ❌ High
**Outcome Reporting**: ✅ Commonly reported / ⚠️ Sometimes / ❌ Rare
**Data Extractability**: ✅ Easy / ⚠️ Moderate / ❌ Difficult

**Recommendation**:
- ✅ **PROCEED** - This looks feasible! [X] studies expected, clear gap identified.
- ⚠️ **PROCEED WITH CAUTION** - [Specific concern]. Plan for [mitigation strategy].
- ❌ **REVISE PICO** - [Fatal flaw]. Suggested changes: [...]

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**Next Steps**:
1. Run 4-hour formal feasibility assessment (see `ma-topic-intake/references/feasibility-checklist.md`)
2. If GO, proceed to protocol development

Does this capture what you want to study? Any adjustments?

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Phase 5: Save to TOPIC.txt (WITH METADATA)

Once confirmed, save enhanced format with feasibility notes:

# Write the finalized topic
cat > projects/<project-name>/TOPIC.txt << 'EOF'
# Meta-Analysis Topic
# Generated: [date]
# Feasibility: [Quick-check passed]

## Research Question
[Full PICO question]

## PICO Elements

**Population**: [detailed]
**Intervention**: [detailed]
**Comparator**: [detailed]
**Outcomes**:
- Primary: [main]
- Secondary: [list]

## Study Design
Randomized controlled trials (RCTs)
[Include observational if justified: reason]

## Feasibility Notes (from brainstorming)
- Estimated studies: ~[X] RCTs
- Existing reviews: [status]
- Heterogeneity risk: [Low/Moderate/High] - [reason]
- Data concerns: [any warnings]
- Recommended next step: 4-hour formal feasibility assessment

## Analysis Type
[pairwise / nma]
- If NMA: Justification: [≥3 treatments with connected comparisons]

## Search Strategy Notes
- Databases: PubMed, Scopus, Embase, Cochrane
- Date range: [suggest based on literature scan]
- Language: English [+ others if justified]

## Potential Challenges
[List any red flags identified during brainstorming]

## Mitigation Strategies
[How to address challenges above]

---
**Status**: Ready for 4-hour feasibility assessment
**Created by**: Brainstorming session [date]
EOF

Then say:

✅ Topic saved to projects/<project-name>/TOPIC.txt

🚦 Next Step (MANDATORY): Run the 4-hour feasibility assessment

This will:

• Validate the quick checks I just did
• Extract data from 3 pilot studies
• Score feasibility (0-16 points)
• Give you a GO/REVISE/STOP decision

Why: This prevents 10-40 hours of wasted work on unanswerable questions.

Ready to start the feasibility assessment now, or want to refine the topic first?

---

🎓 Knowledge Base for AI Agents

Common Failure Patterns (AVOID THESE)

❌ Failure	Why It Fails	✅ How to Prevent
"All cancer treatments"	Too broad, can't pool	Narrow to specific cancer + specific treatment class
"Drug X in rare disease"	<5 studies exist	Check study count BEFORE finalizing
"Improvement in symptoms"	Outcome not quantifiable	Require specific scale (e.g., HAM-D score)
"Any control group"	High heterogeneity	Specify one comparator type
"Quality of life" (vague)	Different scales across studies	Specify QoL instrument (e.g., SF-36, EQ-5D)

Red Flags Checklist (Check BEFORE Phase 4)

• PICO too broad? (can't pool diverse studies)
• PICO too narrow? (<5 studies likely)
• Outcome not commonly reported? (will have missing data)
• Recent systematic review exists? (redundant work)
• High heterogeneity expected? (I²>75% likely)
• Data extraction difficulty? (outcome buried in text, not tables)

If ANY red flag = YES, address BEFORE saving TOPIC.txt

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📚 Success Examples Library (Offer as Templates)

✅ Example 1: Well-Scoped Topic (GOOD)

Research Question: Are SSRIs more effective than SNRIs for reducing depression symptoms in adults with major depressive disorder?

Why good:

• ✅ Specific intervention classes (not "antidepressants")
• ✅ Clear population (adults with MDD)
• ✅ Quantifiable outcome (depression symptoms on validated scales)
• ✅ Expected studies: 20+ RCTs

Feasibility: HIGH (14/16 points)

---

✅ Example 2: Updating Existing Review (GOOD)

Research Question: Efficacy of digital CBT vs face-to-face CBT for anxiety disorders: An updated meta-analysis

Why good:

• ✅ Builds on existing review (Cochrane 2021)
• ✅ Clear comparison
• ✅ New studies published since 2021
• ✅ Well-defined outcome (anxiety scales)

Feasibility: HIGH (13/16 points)

---

⚠️ Example 3: Marginal Topic (Needs Revision)

Research Question: Are probiotics effective for improving gut health in adults?

Why marginal:

• ⚠️ "Probiotics" too vague (many strains)
• ⚠️ "Gut health" not quantifiable
• ⚠️ High heterogeneity expected

Revision needed: → Narrow to: "Lactobacillus rhamnosus GG for reducing IBS symptom severity (IBS-SSS scale) in adults"

Feasibility: After revision: MODERATE (11/16 → 14/16)

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❌ Example 4: Unfeasible Topic (STOP)

Research Question: Effectiveness of mindfulness meditation for any mental health condition

Why unfeasible:

• ❌ Too broad ("any mental health condition")
• ❌ Outcome not specified
• ❌ Heterogeneity impossibly high (I²>90% certain)
• ❌ Can't pool studies meaningfully

Recommendation: STOP and choose a specific condition (e.g., depression OR anxiety, not both)

Feasibility: FAIL (4/16 points)

---

🤖 Self-Prompts for AI Agents (Use Throughout)

After Each PICO Element

Ask yourself:

1. "If I were doing this meta-analysis, would I be confident finding ≥5 studies?"
2. "Can I name 2-3 actual RCTs that fit this PICO right now?"
3. "Is this PICO specific enough to pool, but broad enough to find studies?"

If answer to ANY is "no" → flag to user immediately

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Before Presenting Final Topic

Ask yourself:

1. "Have I checked for recent systematic reviews?" (Yes/No)
2. "Have I estimated study count?" (Yes/No)
3. "Have I warned about heterogeneity if applicable?" (Yes/No)
4. "Have I flagged outcome reporting concerns?" (Yes/No)
5. "Would I bet $100 this topic will succeed?" (Yes/No)

If answer to ANY is "no" → go back and fix

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When User Pushes Back on Warnings

Your response template:

I understand you're excited about [topic]! My role is to help you succeed, which means being honest about challenges upfront. Here's what I'm concerned about: [specific issue].

Options:

1. Proceed anyway - But let's plan mitigation strategies
2. Revise slightly - [Specific suggestion to address concern]
3. Run quick feasibility check now - 10 min to validate my concern

What feels right to you?

Never: Blindly agree to unfeasible topics. Always: Offer alternatives.

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📞 When to Suggest Formal Feasibility Assessment

Trigger these situations:

1. Uncertain about study count - "Let's run the 4-hour assessment to get exact numbers"
2. Moderate heterogeneity risk - "Pilot extraction will show if pooling is feasible"
3. User is risk-averse - "4 hours now saves 10-40 hours later"
4. This is user's first meta-analysis - "Standard practice, prevents wasted effort"
5. Feasibility score borderline (10-12 points) - "Assessment will give definitive answer"

How to pitch it:

🎯 Recommended: Before investing weeks in this project, let's run a 4-hour feasibility assessment. This will:

• Validate my quick checks (study count, heterogeneity)
• Extract data from 3 pilot studies (see if outcome is reported)
• Score feasibility 0-16 (≥12 = GO)
• Save 10-40 hours if topic needs revision

Want to do this now? It's in ma-topic-intake/references/feasibility-checklist.md

---

🎁 Deliverables at End of Brainstorming

When user says "looks good!", provide:

1. ✅ TOPIC.txt file (saved to projects/<project-name>/TOPIC.txt)
2. ✅ Feasibility quick-check summary (in TOPIC.txt metadata)
3. ✅ Next steps (formal 4-hour assessment)
4. ✅ List of potential challenges (from your checks)
5. ✅ Suggested mitigation strategies

Template response:

🎉 Brainstorming complete!

✅ Saved: projects/<project-name>/TOPIC.txt (includes PICO + feasibility notes)

📋 Quick Feasibility Check:

• Study volume: ~[X] RCTs ✅
• Recent reviews: [status] ✅/⚠️
• Heterogeneity: [Low/Moderate] ✅/⚠️
• Outcome reporting: [Good/Moderate] ✅/⚠️

🚦 Recommendation: [PROCEED / PROCEED WITH CAUTION / REVISE]

⚠️ Potential Challenges:

• [List specific concerns from checks]

🛡️ Mitigation Strategies:

• [How to address each concern]

📍 Next Steps:

1. MANDATORY: Run 4-hour formal feasibility assessment (ma-topic-intake/references/feasibility-checklist.md)
2. If GO: Proceed to protocol development (Stage 01)
3. If REVISE: Come back and we'll adjust PICO
4. If STOP: Choose a different topic (I'll help!)

Ready to start the feasibility assessment now? Or want to refine anything first?

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🔄 Iterative Refinement (When User Says "Not Quite Right")

If user isn't satisfied with the topic:

1. Ask specifically: "What feels off? [Population / Intervention / Comparator / Outcome / Scope]?"
2. Offer 2-3 alternatives based on their answer
3. Re-run feasibility checks for each alternative
4. Let user choose the most appealing option

Example:

Okay, let's adjust! Which element feels off?

A. Population too narrow/broad

• Narrower: [example]
• Broader: [example]

B. Intervention not quite right

• Alternative 1: [example]
• Alternative 2: [example]

C. Outcome not what you want

• Instead of [X], try [Y]?

D. Comparison not ideal

• Switch to [alternative comparator]?

Tell me which letter + option, and I'll revise!

---

🎯 Success Metrics (For This Skill)

You've succeeded when:

• ✅ User has a feasible topic (not just any topic)
• ✅ Quick feasibility checks completed (study count, reviews, heterogeneity)
• ✅ User understands next steps (4-hour assessment)
• ✅ Potential challenges flagged early (not discovered later)
• ✅ TOPIC.txt saved with metadata (not just PICO alone)

You've failed when:

• ❌ User starts extraction, then discovers <3 studies exist
• ❌ User discovers later that outcome isn't reported
• ❌ High heterogeneity makes pooling impossible (should've warned)
• ❌ Recent review makes work redundant (should've checked)

Measure: 4-hour feasibility assessment should pass ≥80% of the time for topics you help create. If not, you're being too lenient.

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📖 Additional Resources for Users

When user asks "How do I do X?":

Question	Point Them To
"How to search PubMed?"	ma-search-bibliography/references/api-setup.md
"What's a good PICO?"	This skill's Example Library above
"How many studies do I need?"	Rule of thumb: ≥5 RCTs minimum, ≥10 ideal
"What if outcome isn't reported?"	Contact authors, or use surrogate outcome
"What's the 4-hour assessment?"	ma-topic-intake/references/feasibility-checklist.md
"Can I skip feasibility check?"	NO - It's mandatory. Saves 10-40 hours later.

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Version: 2.0 (Enhanced) Date: 2026-02-17 Changes from v1.0:

• Added Phase 0 (pre-assessment)
• Added instant feasibility checks after each PICO element
• Added heterogeneity risk assessment
• Added data availability checks
• Added AI self-check prompts throughout
• Added success examples library
• Added TOPIC.txt metadata (not just PICO)
• Added formal handoff to 4-hour assessment
• Added failure patterns and prevention strategies