find-cohort-gap

Files (8)

• SKILL.md
• cohort_profile_template.md · references/
• onepager_template.md · references/
• pattern_scoring_rubric.md · references/
• saturation_query_templates.md · references/
• build_cohort_profile.py · scripts/
• skill.yml
• test_cohort_profile.sh · tests/

SKILL.md (first lines)

# 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:

From Aperivue/medsci-skills, MIT.

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