find-cohort-gap git:20260930.c83ad00A

Immutable. This exact content never changes and is served at /api/v1/blob/9e5e7eb488da8985.

---
name: find-cohort-gap
description: >
  Research gap finder for longitudinal cohort databases. Profiles cohort strengths,
  matches PI expertise, scans literature saturation, and outputs ranked topic proposals
  with gap evidence. Works with any cohort: NHIS, UK Biobank, institutional EMR, health
  checkup registries, or disease-specific registries.
triggers: cohort gap, research topic, DB 주제, 코호트 갭, gap analysis, 연구주제 찾기, find research gap, 주제 발굴
tools: Read, Write, Edit, Bash, Grep, Glob
model: opus
---

# Find-Cohort-Gap Skill

Output directory: user-specified (default: current working directory).

## Phase 0: Cohort Intake

The cohort does not have to be one this skill has heard of. Route on what the user
actually has.

| The user has… | Do this |
|---------------|---------|
| A **named public cohort** (NHIS, UK Biobank, KNHANES, …) | Fill the profile from published documentation. Cite the source for every field. |
| A **codebook / data dictionary / CSV export** of their own registry or EMR extract | Run the input adapter below. This is the common case — an institutional registry or single-centre export that no public documentation describes. |
| A **review, guideline, or preprint** defining the clinical domain | Attach it as domain context (`--context`), as a file or a URL. |

### Input adapter (local codebook / documents)

```bash
python3 "${CLAUDE_SKILL_DIR}/scripts/build_cohort_profile.py" \
  --codebook data_dictionary.csv \
  --context narrative_review.pdf --context https://example.org/guideline \
  --cohort-name "Institutional CT registry" --out-dir .
```

Formats: `.csv` / `.tsv` / `.json` / `.md` / `.txt` (stdlib), `.xlsx` (needs `openpyxl`),
`.pdf` (needs `pdftotext`). A `.csv` is auto-detected as a **codebook** (rows are
variables) or a **data export** (the header row is the variable list). Writes
`cohort_profile.md` + `cohort_profile.json` (+ `context_extract.md`).

**Do not read the codebook yourself and summarise it.** A paraphrased, merged, or invented
variable poisons every downstream claim — the intersection matrix, the feasibility gate, and
eventually the manuscript's Methods. The adapter enumerates variables verbatim with provenance
(`file:row`). Read `cohort_profile.md`; do not re-derive it.

The adapter infers, and shows its work for, the **variable cluster map**, **serial /
repeated-measure groups** (evidence for P1 Longitudinal Advantage), and **endpoint
candidates** (evidence for P2 Endpoint Upgrade). A variable matching no cluster keyword is
left `unclassified` — review those, since the lexicon is not exhaustive.

### What the adapter cannot know — ASK, never guess

A codebook does not state any of the following; each is emitted as `[UNKNOWN - ask the user]`:

1. **Sample size** (N at baseline, N with follow-up)
2. **Time span** (enrollment period, follow-up duration, measurement intervals)
3. **Known limitations** (healthy volunteer bias, attrition, missing-data patterns)
4. **Existing publications** from this cohort (to avoid duplicating them)
5. **IRB status and data-access route**

Collect these from the user before Phase 2, because a guessed N flows into the Phase 5
feasibility gate and makes it pass or fail for a reason unrelated to the cohort. Also confirm
the **setting** (institution type, country, population type) and any **special strengths**
the variable names cannot reveal — registry linkage, biobank availability, a distinctive
population.

**Gate:** Present the cohort profile summary, including the `[UNKNOWN]` list and the
unclassified variables. Confirm before proceeding.

## Phase 1: PI/CA Profiling

Profile the intended PI or corresponding author to find topic-expertise alignment. If no PI is
specified, skip this phase and use variable clusters alone in Phase 2.

1. **Search PubMed** for the PI's recent publications (last 5 years) with `/search-lit`'s
   E-utilities script:
   `bash "${CLAUDE_SKILL_DIR}/../search-lit/references/pubmed_eutils.sh" search "AuthorLastName AuthorFirstInitial[Author]" 30`
   Extract top keyword clusters from titles/abstracts.
2. **Identify specialty signals**: academic society positions (president, board member,
   editor), subspecialty focus areas, preferred journal tiers.
3. **Build a PI keyword map**: 5-10 keyword clusters ranked by publication frequency.

**Output:** PI profile card (name, affiliation, top keywords, society roles, preferred journals).

## Phase 2: Intersection Matrix

Cross cohort variable clusters with PI expertise to generate candidate topics.

### Method

Create a matrix: rows = DB variable clusters, columns = PI keyword clusters.
Score each cell 0-3:
- **3**: PI has published in this exact intersection (direct match)
- **2**: PI's subspecialty covers this area (strong relevance)
- **1**: Tangential connection (possible but needs framing)
- **0**: No connection

### Candidate Generation

1. Extract all cells scoring 2-3 as primary candidates.
2. For cells scoring 1, apply the **A-B substitution test**: "Has someone published
   [this analysis] with [a different exposure/outcome] in a similar cohort?" If yes,
   substituting the PI's specialty variable creates a viable candidate.
3. Generate 20-40 candidate topic statements in PICO format — **P** population from the
   cohort, **E** exposure/predictor variable(s), **C** comparison group, **O** outcome
   (preferably hard endpoint).

### Discipline Alignment Filter

Before saturation scanning, identify the intended first author's department/specialty. The
primary exposure variable must belong to that discipline (e.g., radiology first author →
imaging variable as the primary exposure). **Kill candidates where the primary exposure is
outside the first author's discipline** — a strong PI match alone is insufficient if the first
author cannot claim ownership of the core variable.

**Gate:** Present the intersection matrix and top 20 candidates (post-discipline
filter). User selects 8-12 for saturation scanning.

## Phase 3: Literature Saturation Scan

For each selected candidate, determine how saturated the literature is.

### Search Strategy

For each candidate:
1. Build a PubMed query: `(exposure terms) AND (outcome terms) AND (cohort OR longitudinal OR prospective)`
2. Execute search via `/search-lit` E-utilities.
3. Count total results, then ask the **Critical Filter** question — **"Has anyone published
   this with serial/repeated measurements?"** — and check whether a meta-analysis exists.
   Grade:

| Papers | Longitudinal papers | MA exists? | Grade | Interpretation |
|--------|---------------------|------------|-------|----------------|
| 0-2 | 0 | No | **Blue Ocean** | First report possible. Verify the topic has audience interest. |
| 3-10 | 0 | No | **Green Field** | **Optimal zone** — established interest, longitudinal gap wide open. |
| 10-30 | 0 | No | **Green Field** (upgraded from Yellow) | As above. |
| 3-30 | 1+ | No | **Yellow** | Viable only with very specific angle (unique population, novel endpoint). |
| 30+ | Any | No | **Yellow** (borderline Red) | As above. |
| Any | Any | Yes, outdated (>5 yr) or limited scope | **Yellow** | As above. |
| Any | Any | Yes, recent | **Red** | Avoid unless doing NMA or using truly unique data. |

### "So What" Test

For each candidate, articulate 2-3 potential clinical implications of the findings.
If you cannot state why a clinician or policymaker would care about the result,
the topic fails regardless of gap score.

**Output:** Saturation table with grade, paper count, longitudinal gap status, and
"So What" statement for each candidate.

**Gate:** Present saturation results. User selects 3-5 finalists for deep scoring.

## Phase 4: 6-Pattern Scoring + Comparison Table

Apply the 6-Pattern framework to each finalist. Score each pattern 0 or 1.

### 6 Patterns (Universal)

Read the detailed rubric at `${CLAUDE_SKILL_DIR}/references/pattern_scoring_rubric.md` and score
each finalist on **P1** Longitudinal Advantage, **P2** Endpoint Upgrade, **P3** Cohort Uniqueness,
**P4** PI-Topic Alignment (skip if no PI specified), **P5** Comparison Table Gaps (3+ features
unique to THIS STUDY in the table below), and **P6** Complementary Design.

### Comparison Table Construction

For each finalist, build a table comparing the top 3-5 existing papers against THIS STUDY,
one row per feature (design, N, serial data, hard endpoint, population, ethnicity, subgroup
analysis, …) — the rubric's P5 lists the construction steps and differentiator categories.
Cite existing papers only with a `/search-lit`-confirmed DOI or PMID; otherwise mark the
reference `[UNVERIFIED - NEEDS MANUAL CHECK]`.

### Score Interpretation

| Total Score | Recommendation |
|-------------|----------------|
| 5-6 | Top-tier journal target (Lancet sub, JACC, J Hepatol level) |
| 3-4 | Specialty journal target (solid publication) |
| 1-2 | Restructure or kill — find a stronger angle before proceeding |

**Gate:** Present scoring results and comparison tables. User approves final ranking.

## Phase 5: Feasibility Gate

For each scored finalist, verify practical feasibility.

### Checks

1. **Sample size adequacy**:
   - Cox and logistic regression: minimum 10 events per predictor variable (EPV rule)
   - For large cohorts (N>100K): warn about p-value inflation — statistically significant
     results are nearly guaranteed, so focus on **effect size thresholds** (e.g., HR >1.2
     or <0.8 for clinical relevance)
   - Consider negative control strategy (EPCV) for very large samples
2. **Missing data**: key exposure variable <20% missing acceptable; key outcome <5% missing;
   if serial data, assess the attrition pattern (MCAR/MAR/MNAR)
3. **Follow-up adequacy**: the outcome must have plausible latency within available
   follow-up — cancer outcomes minimum 5 years, CVD events minimum 3 years, mortality
   minimum 5 years
4. **Operational definition**:
   - Can the exposure be defined from available variables?
   - For claims data: ICD codes alone = 40-60% accuracy. Require combination
     strategy (diagnosis + prescription + visit frequency + special codes)
   - Cross-check expected prevalence against known epidemiological data
   - Flag any clinical definition, diagnostic criterion, or guideline claim you cannot
     source with `[VERIFY]` and ask the user.
5. **IRB/ethics**: is the data already IRB-approved for this type of analysis? Any
   additional approvals needed for data linkage?
6. **Disease Novelty Bonus** (informational, not Go/No-Go): idiopathic etiology or debated
   mechanism → higher journal interest; established mechanism → needs stronger
   methodological novelty

### Decision

- **Go**: All checks pass.
- **Conditional Go**: Minor issues solvable (e.g., missing data manageable with imputation).
- **No-Go**: Fatal flaw (insufficient events, no valid endpoint, key variable unavailable).

**Output:** Feasibility report for each finalist with Go/Conditional/No-Go status.

## Phase 6: Output — Ranked Proposals + One-Pagers

### Ranked Summary Table

```
| Rank | Topic (PICO) | Saturation | 6-Pattern Score | Feasibility | Target Journal | Timeline |
|------|--------------|------------|-----------------|-------------|----------------|----------|
| 1 | ... | Green (0 longitudinal) | 5/6 | Go | JACC | 6 months |
| 2 | ... | Blue (0 papers) | 3/6 | Conditional | Radiology | 8 months |
```

### One-Pager for Each Finalist

Fill every section of the template at `${CLAUDE_SKILL_DIR}/references/onepager_template.md`;
give the target journal with its rationale (PI alignment, scope match, gap fit). Save one-pagers
as markdown files: `{output_dir}/gap_proposal_{rank}_{short_topic}.md`

**Downstream:** output feeds into `/design-study` → `/write-paper`.